Secure computing for java and dot net

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Secure computing for java/ dot net
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1.A simple text-based shoulder surfing resistant graphical
password scheme.
Since conventional password schemes are vulnerable to shoulder surfing, many shoulder
surfing resistant graphical password schemes have been proposed. However, as most
users are more familiar with textual passwords than pure graphical passwords, text-based
graphical password schemes have been proposed. Unfortunately, none of existing text-
based shoulder surfing resistant graphical password schemes is both secure and efficient
enough. In this paper, we propose an improved text-based shoulder surfing resistant
graphical password scheme by using colors. In the proposed scheme, the user can easily
and efficiently login system. Next, we analyze the security and usability of the proposed
scheme, and show the resistance of the proposed scheme to shoulder surfing and
accidental login.
2. Craigslist Scams and Community Composition:
Investigating Online Fraud Victimization.
Synopsis:
Offline, crime and resulting victimization is not individual incidence. It is also hampered or
encouraged by the community in which it is situated. Are community characteristics relevant
for victimization online? This paper examines the prevalence of Craigslist-based
(automobile) scams across 30 American cities. Our methodology analyses historical scam
data and its relationship with economic, structural, and cultural characteristics of the
communities that are exposed to fraudulent advertising. We find that Craigslist scams are
not random, but targeted towards specific communities. The resulting policy insight is for
creating public awareness campaigns addressing educated white males, as they are the
most vulnerable.
3.A Learning-Based Approach to Reactive Security
Synopsis:
Despite the conventional wisdom that proactive security is superior to reactive security, we
show that reactive security can be competitive with proactive security as long as the

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reactive defender learns from past attacks instead of myopically overreacting to the last
attack. Our game-theoretic model follows common practice in the security literature by
making worst case assumptions about the attacker: we grant the attacker complete
knowledge of the defender's strategy and do not require the attacker to act rationally. In this
model, we bound the competitive ratio between a reactive defense algorithm (which is
inspired by online learning theory) and the best fixed proactive defense. Additionally, we
show that, unlike proactive defenses, this reactive strategy is robust to a lack of information
about the attacker's incentives and knowledge.
4. A Distributed Algorithm for Finding All Best Swap Edges
of a Minimum-Diameter Spanning Tree.
Synopsis:
Communication in networks suffers if a link fails. When the links are edges of a tree that has
been chosen from an underlying graph of all possible links, a broken link even disconnects
the network. Most often, the link is restored rapidly. A good policy to deal with this sort of
transient link failures is swap rerouting, where the temporarily broken link is replaced by a
single swap link from the underlying graph. A rapid replacement of a broken link by a swap
link is only possible if all swap links have been precomputed. The selection of high-quality
swap links is essential; it must follow the same objective as the originally chosen
communication subnetwork. We are interested in a minimum-diameter tree in a graph with
edge weights (so as to minimize the maximum travel time of messages). Hence, each swap
link must minimize (among all possible swaps) the diameter of the tree that results from
swapping. We propose a distributed algorithm that efficiently computes all of these swap
links, and we explain how to route messages across swap edges with a compact routing
scheme. Finally, we consider the computation of swap edges in an arbitrary spanning tree,
where swap edges are chosen to minimize the time required to adapt routing in case of a
failure, and give efficient distributed algorithms for two variants of this problem.
5. Efficiently Outsourcing Multiparty Computation Under
Multiple Keys
Synopsis:
Secure multiparty computation enables a set of users to evaluate certain functionalities on
their respective inputs while keeping these inputs encrypted throughout the computation. In
many applications, however, outsourcing these computations to an untrusted server is
desirable, so that the server can perform the computation on behalf of the users.
Unfortunately, existing solutions are either inefficient, rely heavily on user interaction, or
require the inputs to be encrypted under the same public key - drawbacks making the

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employment in practice very limited. We propose a novel technique based on additively
homomorphic encryption that avoids all these drawbacks. This method is efficient, requires
no user interaction whatsoever (except for data upload and download), and allows
evaluating any dynamically chosen function on inputs encrypted under different public keys.
Our solution assumes the existence of two non-colluding but untrusted servers that jointly
perform the computation by means of a cryptographic protocol. This protocol is proven to be
secure in the semi-honest model. By developing application-tailored variants of our
approach, we demonstrate its versatility and apply it in two real-world scenarios from
different domains, privacy-preserving face recognition and private smart metering. We also
give a proof-of-concept implementation to highlight its feasibility.
6. Anomaly Detection via Online Oversampling Principal
Component Analysis.
Synopsis:
Anomaly detection has been an important research topic in data mining and machine
learning. Many real-world applications such as intrusion or credit card fraud detection
require an effective and efficient framework to identify deviated data instances. However,
most anomaly detection methods are typically implemented in batch mode, and thus cannot
be easily extended to large-scale problems without sacrificing computation and memory
requirements. In this paper, we propose an online oversampling principal component
analysis (osPCA) algorithm to address this problem, and we aim at detecting the presence
of outliers from a large amount of data via an online updating technique. Unlike prior
principal component analysis (PCA)-based approaches, we do not store the entire data
matrix or covariance matrix, and thus our approach is especially of interest in online or
large-scale problems. By oversampling the target instance and extracting the principal
direction of the data, the proposed osPCA allows us to determine the anomaly of the target
instance according to the variation of the resulting dominant eigenvector. Since our osPCA
need not perform eigen analysis explicitly, the proposed framework is favored for online
applications which have computation or memory limitations. Compared with the well-known
power method for PCA and other popular anomaly detection algorithms, our experimental
results verify the feasibility of our proposed method in terms of both accuracy and efficiency.
7. Coding for Cryptographic Security Enhancement Using
Stopping Sets.
Synopsis:
In this paper, we discuss the ability of channel codes to enhance cryptographic secrecy.
Toward that end, we present the secrecy metric of degrees of freedom in an attacker's

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knowledge of the cryptogram, which is similar to equivocation. Using this notion of secrecy,
we show how a specific practical channel coding system can be used to hide information
about the ciphertext, thus increasing the difficulty of cryptographic attacks. The system
setup is the wiretap channel model where transmitted data traverse through independent
packet erasure channels (PECs) with public feedback for authenticated automatic repeat-
request (ARQ). The code design relies on puncturing nonsystematic low-density parity-
check (LDPC) codes with the intent of inflicting an eavesdropper with stopping sets in the
decoder. The design amplifies errors when stopping sets occur such that a receiver must
guess all the channel-erased bits correctly to avoid an error rate of one half in the
ciphertext. We extend previous results on the coding scheme by giving design criteria that
reduce the effectiveness of a maximum-likelihood (ML) attack to that of a message-passing
(MP) attack. We further extend security analysis to models with multiple receivers and
collaborative attackers. Cryptographic security is even enhanced by the system when
eavesdroppers have better channel quality than legitimate receivers.
8. Extracting Spread-Spectrum Hidden Data From Digital
Media.
Synopsis:
We consider the problem of extracting blindly data embedded over a wide band in a
spectrum (transform) domain of a digital medium (image, audio, video). We develop a novel
multicarrier/signature iterative generalized least-squares (M-IGLS) core procedure to seek
unknown data hidden in hosts via multicarrier spread-spectrum embedding. Neither the
original host nor the embedding carriers are assumed available. Experimental studies on
images show that the developed algorithm can achieve recovery probability of error close to
what may be attained with known embedding carriers and host autocorrelation matrix.
9. Data-Provenance Verification For Secure Hosts.
Synopsis:
Malicious software typically resides stealthily on a user's computer and interacts
with the user's computing resources. Our goal in this work is to improve the
trustworthiness of a host and its system data. Specifically, we provide a new
mechanism that ensures the correct origin or provenance of critical system
information and prevents adversaries from utilizing host resources. We define data-
provenance integrity as the security property stating that the source where a piece
of data is generated cannot be spoofed or tampered with. We describe a
cryptographic provenance verification approach for ensuring system properties and

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system-data integrity at kernel-level. Its two concrete applications are demonstrated
in the keystroke integrity verification and malicious traffic detection. Specifically, we
first design and implement an efficient cryptographic protocol that enforces
keystroke integrity by utilizing on-chip Trusted Computing Platform (TPM). The
protocol prevents the forgery of fake key events by malware under reasonable
assumptions. Then, we demonstrate our provenance verification approach by
realizing a lightweight framework for restricting outbound malware traffic. This
traffic-monitoring framework helps identify network activities of stealthy malware,
and lends itself to a powerful personal firewall for examining all outbound traffic of a
host that cannot be bypassed.
10. Modeling and Detection of Camouflaging Worm.
Synopsis:
Active worms pose major security threats to the Internet. This is due to the ability of active
worms to propagate in an automated fashion as they continuously compromise computers
on the Internet. Active worms evolve during their propagation, and thus, pose great
challenges to defend against them. In this paper, we investigate a new class of active
worms, referred to as Camouflaging Worm (C-Worm in short). The C-Worm is different from
traditional worms because of its ability to intelligently manipulate its scan traffic volume over
time. Thereby, the C-Worm camouflages its propagation from existing worm detection
systems based on analyzing the propagation traffic generated by worms. We analyze
characteristics of the C-Worm and conduct a comprehensive comparison between its traffic
and nonworm traffic (background traffic). We observe that these two types of traffic are
barely distinguishable in the time domain. However, their distinction is clear in the frequency
domain, due to the recurring manipulative nature of the C-Worm. Motivated by our
observations, we design a novel spectrum-based scheme to detect the C-Worm. Our
scheme uses the Power Spectral Density (PSD) distribution of the scan traffic volume and
its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from
background traffic. Using a comprehensive set of detection metrics and real-world traces as
background traffic, we conduct extensive performance evaluations on our proposed
spectrum-based detection scheme. The performance data clearly demonstrates that our
scheme can effectively detect the C-Worm propagation. Furthermore, we show the
generality of our spectrum-based scheme in effectively detecting not only the C-Worm, but
traditional worms as well.
11. NICE: Network Intrusion Detection and
Countermeasure Selection in Virtual Network Systems.

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Synopsis:
Cloud security is one of most important issues that has attracted a lot of research and
development effort in past few years. Particularly, attackers can explore vulnerabilities of a
cloud system and compromise virtual machines to deploy further large-scale Distributed
Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as
multistep exploitation, low-frequency vulnerability scanning, and compromising identified
vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised
zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds,
the detection of zombie exploration attacks is extremely difficult. This is because cloud
users may install vulnerable applications on their virtual machines. To prevent vulnerable
virtual machines from being compromised in the cloud, we propose a multiphase distributed
vulnerability detection, measurement, and countermeasure selection mechanism called
NICE, which is built on attack graph-based analytical models and reconfigurable virtual
network-based countermeasures. The proposed framework leverages OpenFlow network
programming APIs to build a monitor and control plane over distributed programmable
virtual switches to significantly improve attack detection and mitigate attack consequences.
The system and security evaluations demonstrate the efficiency and effectiveness of the
proposed solution.
12. Detecting Anomalous Insiders in Collaborative
Information Systems.
Synopsis:
Collaborative information systems (CISs) are deployed within a diverse array of environments
that manage sensitive information. Current security mechanisms detect insider threats, but they
are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we
introduce the community anomaly detection system (CADS), an unsupervised learning
framework to detect insider threats based on the access logs of collaborative environments. The
framework is based on the observation that typical CIS users tend to form community structures
based on the subjects accessed (e.g., patients' records viewed by healthcare providers). CADS
consists of two components: 1) relational pattern extraction, which derives community structures
and 2) anomaly prediction, which leverages a statistical model to determine when users have
sufficiently deviated from communities. We further extend CADS into MetaCADS to account for
the semantics of subjects (e.g., patients' diagnoses). To empirically evaluate the framework, we
perform an assessment with three months of access logs from a real electronic health record
(EHR) system in a large medical center. The results illustrate our models exhibit significant
performance gains over state-of-the-art competitors. When the number of illicit users is low,
MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to
hiding in a crowd, such that CADS is more prudent.

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13. Nymble: Blocking Misbehaving Users in Anonymizing
Networks.
Synopsis:
Anonymizing networks such as Tor allow users to access Internet services
privately by using a series of routers to hide the client's IP address from the server.
The success of such networks, however, has been limited by users employing this
anonymity for abusive purposes such as defacing popular Web sites. Web site
administrators routinely rely on IP-address blocking for disabling access to
misbehaving users, but blocking IP addresses is not practical if the abuser routes
through an anonymizing network. As a result, administrators block all known exit
nodes of anonymizing networks, denying anonymous access to misbehaving and
behaving users alike. To address this problem, we present Nymble, a system in
which servers can “blacklist” misbehaving users, thereby blocking users without
compromising their anonymity. Our system is thus agnostic to different servers'
definitions of misbehavior-servers can blacklist users for whatever reason, and the
privacy of blacklisted users is maintained.
14. On Inference-Proof View Processing of XML
Documents.
Synopsis:
This work aims at treating the inference problem in XML documents that are assumed to
represent potentially incomplete information. The inference problem consists in providing a
control mechanism for enforcing inference-usability confinement of XML documents. More
formally, an inference-proof view of an XML document is required to be both
indistinguishable from the actual XML document to the clients under their inference
capabilities, and to neither contain nor imply any confidential information. We present an
algorithm for generating an inference-proof view by weakening the actual XML document,
i.e., eliminating confidential information and other information that could be used to infer
confidential information. In order to avoid inferences based on the schema of the XML
documents, the DTD of the actual XML document is modified according to the weakening
operations as well, such that the modified DTD conforms with the generated inference-proof
view.

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15. Detecting Spam Zombies by Monitoring Outgoing
Messages.
Synopsis:
Compromised machines are one of the key security threats on the Internet; they are often
used to launch various security attacks such as spamming and spreading malware, DDoS,
and identity theft. Given that spamming provides a key economic incentive for attackers to
recruit the large number of compromised machines, we focus on the detection of the
compromised machines in a network that are involved in the spamming activities, commonly
known as spam zombies. We develop an effective spam zombie detection system named
SPOT by monitoring outgoing messages of a network. SPOT is designed based on a
powerful statistical tool called Sequential Probability Ratio Test, which has bounded false
positive and false negative error rates. In addition, we also evaluate the performance of the
developed SPOT system using a two-month e-mail trace collected in a large US campus
network. Our evaluation studies show that SPOT is an effective and efficient system in
automatically detecting compromised machines in a network. For example, among the 440
internal IP addresses observed in the e-mail trace, SPOT identifies 132 of them as being
associated with compromised machines. Out of the 132 IP addresses identified by SPOT,
126 can be either independently confirmed (110) or highly likely (16) to be compromised.
Moreover, only seven internal IP addresses associated with compromised machines in the
trace are missed by SPOT. In addition, we also compare the performance of SPOT with two
other spam zombie detection algorithms based on the number and percentage of spam
messages originated or forwarded by internal machines, respectively, and show that SPOT
outperforms these two detection algorithms.
16. PriPAYD: Privacy-Friendly Pay-As-You-Drive
Insurance.
Synopsis:
Pay-As-You-Drive insurance schemes are establishing themselves as the future of car
insurance. However, their current implementations, in which fine-grained location data are
sent to insurers, entail a serious privacy risk. We present PriPAYD, a system where the
premium calculations are performed locally in the vehicle, and only aggregated data are
sent to the insurance company, without leaking location information. Our design is based on
well-understood security techniques that ensure its correct functioning. We discuss the
viability of PriPAYD in terms of cost, security, and ease of certification. We demonstrate that
PriPAYD is possible through a proof-of-concept implementation that shows how privacy can
be obtained at a very reasonable extra cost.

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17. Privacy Preserving Data Analytics for Smart Homes.
Synopsis:
A framework for maintaining security & preserving privacy for analysis of sensor data from
smart homes, without compromising on data utility is presented. Storing the personally
identifiable data as hashed values withholds identifiable information from any computing
nodes. However the very nature of smart home data analytics is establishing preventive
care. Data processing results should be identifiable to certain users responsible for direct
care. Through a separate encrypted identifier dictionary with hashed and actual values of all
unique sets of identifiers, we suggest re-identification of any data processing results.
However the level of re-identification needs to be controlled, depending on the type of user
accessing the results. Generalization and suppression on identifiers from the identifier
dictionary before re-introduction could achieve different levels of privacy preservation. In
this paper we propose an approach to achieve data security & privacy through out the
complete data lifecycle: data generation/collection, transfer, storage, processing and
sharing.
18. Secure Encounter-Based Mobile Social Networks:
Requirements, Designs, and Tradeoffs.
Encounter-based social networks and encounter-based systems link users who share a
location at the same time, as opposed to the traditional social network paradigm of linking
users who have an offline friendship. This new approach presents challenges that are
fundamentally different from those tackled by previous social network designs. In this paper,
we explore the functional and security requirements for these new systems, such as
availability, security, and privacy, and present several design options for building secure
encounter-based social networks. To highlight these challenges, we examine one recently
proposed encounter-based social network design and compare it to a set of idealized
security and functionality requirements. We show that it is vulnerable to several attacks,
including impersonation, collusion, and privacy breaching, even though it was designed
specifically for security. Mindful of the possible pitfalls, we construct a flexible framework for
secure encounter-based social networks, which can be used to construct networks that offer
different security, privacy, and availability guarantees. We describe two example
constructions derived from this framework, and consider each in terms of the ideal
requirements. Some of our new designs fulfill more requirements in terms of system
security, reliability, and privacy than previous work. We also evaluate real-world
performance of one of our designs by implementing a proof-of-concept iPhone application
called MeetUp. Experiments highlight the potential of our system and hint at the
deployability of our designs on a large scale.

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19. A Methodology for Direct and Indirect Discrimination
Prevention in Data Mining.
Synopsis:
Data mining is an increasingly important technology for extracting useful knowledge hidden
in large collections of data. There are, however, negative social perceptions about data
mining, among which potential privacy invasion and potential discrimination. The latter
consists of unfairly treating people on the basis of their belonging to a specific group.
Automated data collection and data mining techniques such as classification rule mining
have paved the way to making automated decisions, like loan granting/denial, insurance
premium computation, etc. If the training data sets are biased in what regards discriminatory
(sensitive) attributes like gender, race, religion, etc., discriminatory decisions may ensue.
For this reason, anti-discrimination techniques including discrimination discovery and
prevention have been introduced in data mining. Discrimination can be either direct or
indirect. Direct discrimination occurs when decisions are made based on sensitive
attributes. Indirect discrimination occurs when decisions are made based on nonsensitive
attributes which are strongly correlated with biased sensitive ones. In this paper, we tackle
discrimination prevention in data mining and propose new techniques applicable for direct
or indirect discrimination prevention individually or both at the same time. We discuss how
to clean training data sets and outsourced data sets in such a way that direct and/or indirect
discriminatory decision rules are converted to legitimate (nondiscriminatory) classification
rules. We also propose new metrics to evaluate the utility of the proposed approaches and
we compare these approaches. The experimental evaluations demonstrate that the
proposed techniques are effective at removing direct and/or indirect discrimination biases in
the original data set while preserving data quality.
20. DoubleGuard: Detecting Intrusions in Multitier Web
Applications.
Synopsis:
Internet services and applications have become an inextricable part of daily life, enabling
communication and the management of personal information from anywhere. To
accommodate this increase in application and data complexity, web services have moved to
a multitiered design wherein the webserver runs the application front-end logic and data are
outsourced to a database or file server. In this paper, we present DoubleGuard, an IDS
system that models the network behavior of user sessions across both the front-end
webserver and the back-end database. By monitoring both web and subsequent database
requests, we are able to ferret out attacks that an independent IDS would not be able to
identify. Furthermore, we quantify the limitations of any multitier IDS in terms of training

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sessions and functionality coverage. We implemented DoubleGuard using an Apache
webserver with MySQL and lightweight virtualization. We then collected and processed real-
world traffic over a 15-day period of system deployment in both dynamic and static web
applications. Finally, using DoubleGuard, we were able to expose a wide range of attacks
with 100 percent accuracy while maintaining 0 percent false positives for static web services
and 0.6 percent false positives for dynamic web services.
21.RITAS: Services for Randomized Intrusion Tolerance.
Synopsis:
Randomized agreement protocols have been around for more than two decades. Often
assumed to be inefficient due to their high expected communication and computation
complexities, they have remained overlooked by the community-at-large as a valid solution
for the deployment of fault-tolerant distributed systems. This paper aims to demonstrate that
randomization can be a very competitive approach even in hostile environments where
arbitrary faults can occur. A stack of randomized intrusion-tolerant protocols is described
and its performance evaluated under several settings in both local-area-network (LAN) and
wide-area-network environments. The stack provides a set of relevant services ranging from
basic communication primitives up to atomic broadcast. The experimental evaluation shows
that the protocols are efficient, especially in LAN environments where no performance
reduction is observed under certain Byzantine faults.
22. SAT: A Security Architecture Achieving Anonymity and
Traceability in Wireless Mesh Networks.
Synopsis:
Anonymity has received increasing attention in the literature due to the users' awareness of
their privacy nowadays. Anonymity provides protection for users to enjoy network services
without being traced. While anonymity-related issues have been extensively studied in
payment-based systems such as e-cash and peer-to-peer (P2P) systems, little effort has
been devoted to wireless mesh networks (WMNs). On the other hand, the network authority
requires conditional anonymity such that misbehaving entities in the network remain
traceable. In this paper, we propose a security architecture to ensure unconditional
anonymity for honest users and traceability of misbehaving users for network authorities in
WMNs. The proposed architecture strives to resolve the conflicts between the anonymity
and traceability objectives, in addition to guaranteeing fundamental security requirements
including authentication, confidentiality, data integrity, and nonrepudiation. Thorough
analysis on security and efficiency is incorporated, demonstrating the feasibility and
effectiveness of the proposed architecture.

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23. Securing Online Reputation Systems Through Trust
Modeling and Temporal Analysis.
Synopsis:
With the rapid development of reputation systems in various online social networks,
manipulations against such systems are evolving quickly. In this paper, we propose scheme
TATA, the abbreviation of joint Temporal And Trust Analysis, which protects reputation
systems from a new angle: the combination of time domain anomaly detection and
Dempster-Shafer theory-based trust computation. Real user attack data collected from a
cyber competition is used to construct the testing data set. Compared with two
representative reputation schemes and our previous scheme, TATA achieves a significantly
better performance in terms of identifying items under attack, detecting malicious users who
insert dishonest ratings, and recovering reputation scores.
24. Securing Online Reputation Systems Through Trust
Modeling and Temporal Analysis.
Synopsis:
With the rapid development of reputation systems in various online social networks,
manipulations against such systems are evolving quickly. In this paper, we propose scheme
TATA, the abbreviation of joint Temporal And Trust Analysis, which protects reputation
systems from a new angle: the combination of time domain anomaly detection and
Dempster-Shafer theory-based trust computation. Real user attack data collected from a
cyber competition is used to construct the testing data set. Compared with two
representative reputation schemes and our previous scheme, TATA achieves a significantly
better performance in terms of identifying items under attack, detecting malicious users who
insert dishonest ratings, and recovering reputation scores.
25. Ensuring Distributed Accountability for Data Sharing in
the Cloud.
Synopsis:
Cloud computing enables highly scalable services to be easily consumed over the Internet
on an as-needed basis. A major feature of the cloud services is that users' data are usually
processed remotely in unknown machines that users do not own or operate. While enjoying
the convenience brought by this new emerging technology, users' fears of losing control of
their own data (particularly, financial and health data) can become a significant barrier to the

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wide adoption of cloud services. To address this problem, in this paper, we propose a novel
highly decentralized information accountability framework to keep track of the actual usage
of the users' data in the cloud. In particular, we propose an object-centered approach that
enables enclosing our logging mechanism together with users' data and policies. We
leverage the JAR programmable capabilities to both create a dynamic and traveling object,
and to ensure that any access to users' data will trigger authentication and automated
logging local to the JARs. To strengthen user's control, we also provide distributed auditing
mechanisms. We provide extensive experimental studies that demonstrate the efficiency
and effectiveness of the proposed approaches.
26. Secure Overlay Cloud Storage with Access Control and
Assured Deletion.
Synopsis:
We can now outsource data backups off-site to third-party cloud storage services so
as to reduce data management costs. However, we must provide security guarantees
for the outsourced data, which is now maintained by third parties. We design and
implement FADE, a secure overlay cloud storage system that achieves fine-grained,
policy-based access control and file assured deletion. It associates outsourced files
with file access policies, and assuredly deletes files to make them unrecoverable to
anyone upon revocations of file access policies. To achieve such security goals, FADE
is built upon a set of cryptographic key operations that are self-maintained by a
quorum of key managers that are independent of third-party clouds. In particular,
FADE acts as an overlay system that works seamlessly atop today's cloud storage
services. We implement a proof-of-concept prototype of FADE atop Amazon S3, one
of today's cloud storage services. We conduct extensive empirical studies, and
demonstrate that FADE provides security protection for outsourced data, while
introducing only minimal performance and monetary cost overhead. Our work
provides insights of how to incorporate value-added security features into today's
cloud storage services.
27. Security Analysis of a Single Sign-On Mechanism for
Distributed Computer Networks.
Synopsis:

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Single sign-on (SSO) is a new authentication mechanism that enables a legal user with a
single credential to be authenticated by multiple service providers in a distributed computer
network. Recently, Chang and Lee proposed a new SSO scheme and claimed its security
by providing well-organized security arguments. In this paper, however, we demonstrative
that their scheme is actually insecure as it fails to meet credential privacy and soundness of
authentication. Specifically, we present two impersonation attacks. The first attack allows a
malicious service provider, who has successfully communicated with a legal user twice, to
recover the user's credential and then to impersonate the user to access resources and
services offered by other service providers. In another attack, an outsider without any
credential may be able to enjoy network services freely by impersonating any legal user or a
nonexistent user. We identify the flaws in their security arguments to explain why attacks
are possible against their SSO scheme. Our attacks also apply to another SSO scheme
proposed by Hsu and Chuang, which inspired the design of the Chang-Lee scheme.
Moreover, by employing an efficient verifiable encryption of RSA signatures proposed by
Ateniese, we propose an improvement for repairing the Chang-Lee scheme. We promote
the formal study of the soundness of authentication as one open problem.
28. Security and Privacy-Enhancing Multicloud
Architectures.
Synopsis:
Security challenges are still among the biggest obstacles when considering the adoption
of cloud services. This triggered a lot of research activities, resulting in a quantity of
proposals targeting the various cloud security threats. Alongside with these security issues,
the cloud paradigm comes with a new set of unique features, which open the path toward
novel security approaches, techniques, and architectures. This paper provides a survey on
the achievable security merits by making use of multiple distinct clouds simultaneously.
Various distinct architectures are introduced and discussed according to their security and
privacy capabilities and prospects.
29. Security using colors and Armstrong numbers.
Synopsis:
In real world, data security plays an important role where confidentiality, authentication, integrity,
non repudiation are given importance. The universal technique for providing confidentiality of
transmitted data is cryptography. This paper provides a technique to encrypt the data using a
key involving Armstrong numbers and colors as the password. Three set of keys are used to
provide secure data transmission with the colors acting as vital security element thereby
providing authentication.

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30. SORT: A Self-ORganizing Trust Model for Peer-to-Peer
Systems.
Synopsis:
Open nature of peer-to-peer systems exposes them to malicious activity. Building
trust relationships among peers can mitigate attacks of malicious peers. This paper
presents distributed algorithms that enable a peer to reason about trustworthiness of
other peers based on past interactions and recommendations. Peers create their own
trust network in their proximity by using local information available and do not try
to learn global trust information. Two contexts of trust, service, and
recommendation contexts, are defined to measure trustworthiness in providing
services and giving recommendations. Interactions and recommendations are
evaluated based on importance, recentness, and peer satisfaction parameters.
Additionally, recommender's trustworthiness and confidence about a
recommendation are considered while evaluating recommendations. Simulation
experiments on a file sharing application show that the proposed model can
mitigate attacks on 16 different malicious behavior models. In the experiments,
good peers were able to form trust relationships in their proximity and isolate
malicious peers.
31. Fast and Accurate Annotation of Short Texts with
Wikipedia Pages.
Synopsis:
Several recent software systems have been designed to obtain novel annotation of cross-
referencing text fragments and Wikipedia pages. Tagme is state of the art in this setting and
can accurately manage short textual fragments (such as snippets of search engine results,
tweets, news, or blogs) on the fly.
32. Online Modeling of Proactive Moderation System for
Auction Fraud Detection.
Synopsis:

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We consider the problem of building online machine-learned models for detecting auction
frauds in e-commence web sites. Since the emergence of the world wide web, online
shopping and online auction have gained more and more popularity. While people are
enjoying the benefits from online trading, criminals are also taking advantages to conduct
fraudulent activities against honest parties to obtain illegal profit. Hence proactive fraud-
detection moderation systems are commonly applied in practice to detect and prevent such
illegal and fraud activities. Machine-learned models, especially those that are learned
online, are able to catch frauds more efficiently and quickly than human-tuned rule-based
systems. In this paper, we propose an online probit model framework which takes online
feature selection, coefficient bounds from human knowledge and multiple instance learning
into account simultaneously. By empirical experiments on a real-world online auction fraud
detection data we show that this model can potentially detect more frauds and significantly
reduce customer complaints compared to several baseline models and the human-tuned
rule-based system.
33. Toward Secure Multikeyword Top-k Retrieval over
Encrypted Cloud Data.
Synopsis:
Cloud computing has emerging as a promising pattern for data outsourcing and high-quality
data services. However, concerns of sensitive information on cloud potentially causes
privacy problems. Data encryption protects data security to some extent, but at the cost of
compromised efficiency. Searchable symmetric encryption (SSE) allows retrieval of
encrypted data over cloud. In this paper, we focus on addressing data privacy issues using
SSE. For the first time, we formulate the privacy issue from the aspect of similarity
relevance and scheme robustness. We observe that server-side ranking based on order-
preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we
propose a two-round searchable encryption (TRSE) scheme that supports top-k
multikeyword retrieval. In TRSE, we employ a vector space model and homomorphic
encryption. The vector space model helps to provide sufficient search accuracy, and the
homomorphic encryption enables users to involve in the ranking while the majority of
computing work is done on the server side by operations only on ciphertext. As a result,
information leakage can be eliminated and data security is ensured. Thorough security and
performance analysis show that the proposed scheme guarantees high security and
practical efficiency.
34. TrPF: A Trajectory Privacy-Preserving Framework for
Participatory Sensing.
Synopsis:

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The ubiquity of the various cheap embedded sensors on mobile devices, for example
cameras, microphones, accelerometers, and so on, is enabling the emergence of
participatory sensing applications. While participatory sensing can benefit the individuals
and communities greatly, the collection and analysis of the participators' location and
trajectory data may jeopardize their privacy. However, the existing proposals mostly focus
on participators' location privacy, and few are done on participators' trajectory privacy. The
effective analysis on trajectories that contain spatial-temporal history information will reveal
participators' whereabouts and the relevant personal privacy. In this paper, we propose a
trajectory privacy-preserving framework, named TrPF, for participatory sensing. Based on
the framework, we improve the theoretical mix-zones model with considering the time factor
from the perspective of graph theory. Finally, we analyze the threat models with different
background knowledge and evaluate the effectiveness of our proposal on the basis of
information entropy, and then compare the performance of our proposal with previous
trajectory privacy protections. The analysis and simulation results prove that our proposal
can protect participators' trajectories privacy effectively with lower information loss and
costs than what is afforded by the other proposals.
35. Packet-Hiding Methods for Preventing Selective
Jamming Attacks.
Synopsis:
The open nature of the wireless medium leaves it vulnerable to intentional interference
attacks, typically referred to as jamming. This intentional interference with wireless
transmissions can be used as a launchpad for mounting Denial-of-Service attacks on
wireless networks. Typically, jamming has been addressed under an external threat model.
However, adversaries with internal knowledge of protocol specifications and network
secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this
work, we address the problem of selective jamming attacks in wireless networks. In these
attacks, the adversary is active only for a short period of time, selectively targeting
messages of high importance. We illustrate the advantages of selective jamming in terms of
network performance degradation and adversary effort by presenting two case studies; a
selective attack on TCP and one on routing. We show that selective jamming attacks can be
launched by performing real-time packet classification at the physical layer. To mitigate
these attacks, we develop three schemes that prevent real-time packet classification by
combining cryptographic primitives with physical-layer attributes. We analyze the security of
our methods and evaluate their computational and communication overhead.
36. pCloud: A Distributed System for Practical PIR.
Synopsis:

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Computational Private Information Retrieval (cPIR) protocols allow a client to retrieve one
bit from a database, without the server inferring any information about the queried bit.
These protocols are too costly in practice because they invoke complex arithmetic
operations for every bit of the database. In this paper, we present pCloud, a distributed
system that constitutes the first attempt toward practical cPIR. Our approach assumes a
disk-based architecture that retrieves one page with a single query. Using a striping
technique, we distribute the database to a number of cooperative peers, and leverage their
computational resources to process cPIR queries in parallel. We implemented pCloud on
the PlanetLab network, and experimented extensively with several system parameters. Our
results indicate that pCloud reduces considerably the query response time compared to the
traditional client/server model, and has a very low communication overhead. Additionally, it
scales well with an increasing number of peers, achieving a linear speedup.
37. Two tales of privacy in online social networks.
Synopsis:
Privacy is one of the friction points that emerge when communications are mediated in
online social networks (OSNs). Different communities of computer science researchers
have framed the OSN privacy problem as one of surveillance, institutional privacy, or social
privacy. In tackling these problems, researchers have also treated them as if they were
independent. In this article, the authors argue that the different privacy problems are
entangled and that OSN privacy research would benefit from a more holistic approach.
38. Utility-Privacy Tradeoffs in Databases: An Information-
Theoretic Approach.
Synopsis:
Ensuring the usefulness of electronic data sources while providing necessary privacy
guarantees is an important unsolved problem. This problem drives the need for an
analytical framework that can quantify the privacy of personally identifiable information while
still providing a quantifiable benefit (utility) to multiple legitimate information consumers.
This paper presents an information-theoretic framework that promises an analytical model
guaranteeing tight bounds of how much utility is possible for a given level of privacy and
vice-versa. Specific contributions include: 1) stochastic data models for both categorical and
numerical data; 2) utility-privacy tradeoff regions and the encoding (sanization) schemes
achieving them for both classes and their practical relevance; and 3) modeling of prior
knowledge at the user and/or data source and optimal encoding schemes for both cases.

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39. Persuasive Cued Click-Points: Design, Implementation,
and Evaluation of a Knowledge-Based Authentication
Mechanism.
Synopsis:
This paper presents an integrated evaluation of the Persuasive Cued Click-Points graphical
password scheme, including usability and security evaluations, and implementation
considerations. An important usability goal for knowledge-based authentication systems is
to support users in selecting passwords of higher security, in the sense of being from an
expanded effective security space. We use persuasion to influence user choice in click-
based graphical passwords, encouraging users to select more random, and hence more
difficult to guess, click-points.
40. Privacy-Preserving Enforcement of Spatially Aware
RBAC.
Synopsis:
Several models for incorporating spatial constraints into role-based access control
(RBAC) have been proposed, and researchers are now focusing on the challenge of
ensuring such policies are enforced correctly. However, existing approaches have a major
shortcoming, as they assume the server is trustworthy and require complete disclosure of
sensitive location information by the user. In this work, we propose a novel framework and a
set of protocols to solve this problem. Specifically, in our scheme, a user provides a service
provider with role and location tokens along with a request. The service provider consults
with a role authority and a location authority to verify the tokens and evaluate the policy.
However, none of the servers learn the requesting user's identity, role, or location. In this
paper, we define the protocols and the policy enforcement scheme, and present a formal
proof of a number of security properties..
41. Remote Attestation with Domain-Based Integrity Model
and Policy Analysis.
Synopsis:
We propose and implement an innovative remote attestation framework called DR@FT for
efficiently measuring a target system based on an information flow-based integrity model. With
this model, the high integrity processes of a system are first measured and verified, and these

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processes are then protected from accesses initiated by low integrity processes. Toward
dynamic systems with frequently changed system states, our framework verifies the latest state
changes of a target system instead of considering the entire system information. Our attestation
evaluation adopts a graph-based method to represent integrity violations, and the graph-based
policy analysis is further augmented with a ranked violation graph to support high semantic
reasoning of attestation results. As a result, DR@FT provides efficient and effective attestation
of a system's integrity status, and offers intuitive reasoning of attestation results for security
administrators. Our experimental results demonstrate the feasibility and practicality of DR@FT.
42. Risk-Aware Mitigation for MANET Routing Attacks.
Synopsis:
Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the
dynamic nature of its network infrastructure. Among these attacks, routing attacks have
received considerable attention since it could cause the most devastating damage to
MANET. Even though there exist several intrusion response techniques to mitigate such
critical attacks, existing solutions typically attempt to isolate malicious nodes ba
attacks in MANET. In
this paper, we propose a risk-aware response mechanism to systematically cope with the
identified routing attacks. Our risk-aware approach is based on an extended Dempster-
Shafer mathematical theory of evidence introducing a notion of importance factors. In
addition, our experiments demonstrate the effectiveness of our approach with the
consideration of several performance metrics.
43. Secure Overlay Cloud Storage with Access Control and
Assured Deletion.
Synopsis:
We can now outsource data backups off-site to third-party cloud storage services so as to
reduce data management costs. However, we must provide security guarantees for the
outsourced data, which is now maintained by third parties. We design and implement
FADE, a secure overlay cloud storage system that achieves fine-grained, policy-based
access control and file assured deletion. It associates outsourced files with file access
policies, and assuredly deletes files to make them unrecoverable to anyone upon
revocations of file access policies. To achieve such security goals, FADE is built upon a set
of cryptographic key operations that are self-maintained by a quorum of key managers that
are independent of third-party clouds. In particular, FADE acts as an overlay system that
works seamlessly atop today's cloud storage services. We implement a proof-of-concept

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prototype of FADE atop Amazon S3, one of today's cloud storage services. We conduct
extensive empirical studies, and demonstrate that FADE provides security protection for
outsourced data, while introducing only minimal performance and monetary cost overhead.
Our work provides insights of how to incorporate value-added security features into today's
cloud storage services.
44. SecuredTrust: A Dynamic Trust Computation Model for
Secured Communication in Multiagent Systems
Synopsis:
Security and privacy issues have become critically important with the fast expansion of
multiagent systems. Most network applications such as pervasive computing, grid
computing, and P2P networks can be viewed as multiagent systems which are open,
anonymous, and dynamic in nature. Such characteristics of multiagent systems introduce
vulnerabilities and threats to providing secured communication. One feasible way to
minimize the threats is to evaluate the trust and reputation of the interacting agents. Many
trust/reputation models have done so, but they fail to properly evaluate trust when malicious
agents start to behave in an unpredictable way. Moreover, these models are ineffective in
providing quick response to a malicious agent's oscillating behavior. Another aspect of
multiagent systems which is becoming critical for sustaining good service quality is the even
distribution of workload among service providing agents. Most trust/reputation models have
not yet addressed this issue. So, to cope with the strategically altering behavior of malicious
agents and to distribute workload as evenly as possible among service providers; we
present in this paper a dynamic trust computation model called "SecuredT ” I
paper, we first analyze the different factors related to evaluating the trust of an agent and
then propose a comprehensive quantitative model for measuring such trust. We also
propose a novel load-balancing algorithm based on the different factors defined in our
model. Simulation results indicate that our model compared to other existing models can
effectively cope with strategic behavioral change of malicious agents and at the same time
efficiently distribute workload among the service providing agents under stable condition.
45. Security architecture for cloud networking.
Synopsis:
Cloud computing offers reduced capital expenditure, operational risks, complexity and
maintenance, and increased scalability while providing services at different abstraction
levels, namely Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and
Infrastructure-as-a-Service (IaaS). A new approach called cloud networking adds
networking functionalities to cloud computing and enables dynamic and flexible placement
of virtual resources crossing provider borders. This allows various kinds of optimization,

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e.g., reducing latency or network load. However, this approach introduces new security
challenges. This paper presents a security architecture that enables a user of cloud
networking to define security requirements and enforce them in the cloud networking
infrastructure.
46. Defending against Web Application Vulnerabilities.
Synopsis:
Although no single tool or technique can guard against the host of possible attacks, a
defense-in-depth approach, with overlapping protections, can help secure Web applications.
47. CAM: Cloud-Assisted Privacy Preserving Mobile Health
Monitoring.
Synopsis:
Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile
communications and cloud computing technologies to provide feedback decision support,
has been considered as a revolutionary approach to improving the quality of healthcare
service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on
both clients' privacy and intellectual property of monitoring service providers, which could
deter the wide adoption of mHealth technology. This paper is to address this important
problem and design a cloud-assisted privacy preserving mobile health monitoring system to
protect the privacy of the involved parties and their data. Moreover, the outsourcing
decryption technique and a newly proposed key private proxy reencryption are adapted to
shift the computational complexity of the involved parties to the cloud without compromising
clients' privacy and service providers' intellectual property. Finally, our security and
performance analysis demonstrates the effectiveness of our proposed design.
48. An efficient and provably-secure coercion-resistant e-
voting protocol.
We present an efficient and provably-secure e-voting protocol, which is a variant of the JCJ
e-voting protocol (Juels et al., 2010). It decreases the total number of JCJ's operations from
O(n2) to O(n), where n is the number of votes or voters (whichever is the maximum). Note
that since the operations under consideration are time-consuming (e.g., public-key
encryption), the improvement is quite substantial. As a rough comparison, consider a
nation-wide election with around ten million voters/votes. Assuming each operation takes
one microsecond, and no parallelization is used, one can see a huge difference: our

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protocol tallies the votes in 10 seconds, while the JCJ protocol requires over 3 years to tally
the votes. In order to achieve this level of efficiency, we change the ballot format and the
tallying phase of the JCJ protocol. Moreover, we provide a complexity analysis and a
detailed proof for coercion-resistance of our protocol.
49. A Novel Data Embedding Method Using Adaptive Pixel
Pair Matching.
Synopsis:
This paper proposes a new data-hiding method based on pixel pair matching (PPM). The
basic idea of PPM is to use the values of pixel pair as a reference coordinate, and search a
coordinate in the neighborhood set of this pixel pair according to a given message digit. The
pixel pair is then replaced by the searched coordinate to conceal the digit. Exploiting
modification direction (EMD) and diamond encoding (DE) are two data-hiding methods
proposed recently based on PPM. The maximum capacity of EMD is 1.161 bpp and DE
extends the payload of EMD by embedding digits in a larger notational system. The
proposed method offers lower distortion than DE by providing more compact neighborhood
sets and allowing embedded digits in any notational system. Compared with the optimal
pixel adjustment process (OPAP) method, the proposed method always has lower distortion
for various payloads. Experimental results reveal that the proposed method not only
provides better performance than those of OPAP and DE, but also is secure under the
detection of some well-known steganalysis techniques.
50. A Probabilistic Model of (t,n) Visual Cryptography
Scheme With Dynamic Group.
Synopsis:
The (t, n) visual cryptography (VC) is a secret sharing scheme where a secret image is
encoded into n transparencies, and the stacking of any t out of n transparencies reveals the
secret image. The stacking of t - 1 or fewer transparencies is unable to extract any
information about the secret. We discuss the additions and deletions of users in a dynamic
user group. To reduce the overhead of generating and distributing transparencies in user
changes, this paper proposes a (t, n) VC scheme with unlimited n based on the probabilistic
model. The proposed scheme allows n to change dynamically in order to include new
transparencies without regenerating and redistributing the original transparencies.
Specifically, an extended VC scheme based on basis matrices and a probabilistic model is
proposed. An equation is derived from the fundamental definitions of the (t, n) VC scheme,
( ∞) VC
derived equation. The maximal contrasts with t = 2 to 6 are explicitly solved in this paper.

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51. A Policy Enforcing Mechanism for Trusted Ad Hoc
Networks.
Synopsis:
To ensure fair and secure communication in Mobile Ad hoc Networks (MANETs), the
applications running in these networks must be regulated by proper communication policies.
However, enforcing policies in MANETs is challenging because they lack the infrastructure
and trusted entities encountered in traditional distributed systems. This paper presents the
design and implementation of a policy enforcing mechanism based on Satem, a kernel-level
trusted execution monitor built on top of the Trusted Platform Module. Under this
mechanism, each application or protocol has an associated policy. Two instances of an
application running on different nodes may engage in communication only if these nodes
enforce the same set of policies for both the application and the underlying protocols used
by the application. In this way, nodes can form trusted application-centric networks. Before
allowing a node to join such a network, Satem verifies its trustworthiness of enforcing the
required set of policies. Furthermore, Satem protects the policies and the software enforcing
these policies from being tampered with. If any of them is compromised, Satem disconnects
the node from the network. We demonstrate the correctness of our solution through security
analysis, and its low overhead through performance evaluation of two MANET applications.
52. Enforcing Secure and Privacy-Preserving Information
Brokering in Distributed Information Sharing.
Synopsis:
Today's organizations raise an increasing need for information sharing via on-demand
access. Information brokering systems (IBSs) have been proposed to connect large-scale
loosely federated data sources via a brokering overlay, in which the brokers make routing
decisions to direct client queries to the requested data servers. Many existing IBSs assume
that brokers are trusted and thus only adopt server-side access control for data
confidentiality. However, privacy of data location and data consumer can still be inferred
from metadata (such as query and access control rules) exchanged within the IBS, but little
attention has been put on its protection. In this paper, we propose a novel approach to
preserve privacy of multiple stakeholders involved in the information brokering process. We
are among the first to formally define two privacy attacks, namely attribute-correlation attack
and inference attack, and propose two countermeasure schemes automaton segmentation
and query segment encryption to securely share the routing decision-making responsibility
among a selected set of brokering servers. With comprehensive security analysis and
experimental results, we show that our approach seamlessly integrates security
enforcement with query routing to provide system-wide security with insignificant overhead.

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53. Adaptive Fault-Tolerant QoS Control Algorithms for
Maximizing System Lifetime of Query-Based Wireless
Sensor Networks.
Synopsis:
Data sensing and retrieval in wireless sensor systems have a widespread application in
areas such as security and surveillance monitoring, and command and control in
battlefields. In query-based wireless sensor systems, a user would issue a query and
expect a response to be returned within the deadline. While the use of fault tolerance
mechanisms through redundancy improves query reliability in the presence of unreliable
wireless communication and sensor faults, it could cause the energy of the system to be
quickly depleted. Therefore, there is an inherent trade-off between query reliability versus
energy consumption in query-based wireless sensor systems. In this paper, we develop
adaptive fault-tolerant quality of service (QoS) control algorithms based on hop-by-hop data
“ ” “ ” Q S
requirements while prolonging the lifetime of the sensor system. We develop a
mathematical model for the lifetime of the sensor system as a function of system
“ ” “ ” W
“ ” “ ”
is maximized while satisfying application QoS requirements. Numerical data are presented
and validated through extensive simulation, with physical interpretations given, to
demonstrate the feasibility of our algorithm design.
54. Access control for online social networks third party
application.
Synopsis:
With the development of Web 2.0 technologies, online social networks are able to provide
open platforms to enable the seamless sharing of profile data to enable public developers to
interface and extend the social network services as applications. At the same time, these
open interfaces pose serious privacy concerns as third party applications are usually given
access to the user profiles. Current related research has focused on mainly user-to-user
interactions in social networks, and seems to ignore the third party applications. In this
paper, we present an access control framework to manage third party applications. Our
framework is based on enabling the user to specify the data attributes to be shared with the
application and at the same time be able to specify the degree of specificity of the shared
attributes. We model applications as finite state machines, and use the required user profile
attributes as conditions governing the application execution. We formulate the minimal

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attribute generalization problem and we propose a solution that maps the problem to the
shortest path problem to find the minimum set of attribute generalization required to access
the application services. We assess the feasibility of our approach by developing a proof-of-
concept implementation and by conducting user studies on a widely-used social network
platform.
55. Data Hiding in Motion Vectors of Compressed Video
Based on Their Associated Prediction Error.
Synopsis:
This paper deals with data hiding in compressed video. Unlike data hiding in images and
raw video which operates on the images themselves in the spatial or transformed domain
which are vulnerable to steganalysis, we target the motion vectors used to encode and
reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in
compressed video. The choice of candidate subset of these motion vectors are based on
their associated macroblock prediction error, which is different from the approaches based
on the motion vector attributes such as the magnitude and phase angle, etc. A greedy
adaptive threshold is searched for every frame to achieve robustness while maintaining a
low prediction error level. The secret message bitstream is embedded in the least significant
bit of both components of the candidate motion vectors. The method is implemented and
tested for hiding data in natural sequences of multiple groups of pictures and the results are
evaluated. The evaluation is based on two criteria: minimum distortion to the reconstructed
video and minimum overhead on the compressed video size. Based on the aforementioned
criteria, the proposed method is found to perform well and is compared to a motion vector
attribute-based method from the literature.
56. Non-Cooperative Location Privacy
Synopsis:
In mobile networks, authentication is a required primitive for most security
protocols. Unfortunately, an adversary can monitor pseudonyms used for
authentication to track the location of mobile nodes. A frequently proposed solution
to protect location privacy suggests that mobile nodes collectively change their
pseudonyms in regions called mix zones. This approach is costly. Self-interested
mobile nodes might, thus, decide not to cooperate and jeopardize the achievable
location privacy. In this paper, we analyze non-cooperative behavior of mobile nodes
by using a game-theoretic model, where each player aims at maximizing its location

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privacy at a minimum cost. We obtain Nash equilibria in static n-player complete
information games. As in practice mobile nodes do not know their opponents'
payoffs, we then consider static incomplete information games. We establish that
symmetric Bayesian-Nash equilibria exist with simple threshold strategies. By means
of numerical results, we predict behavior of selfish mobile nodes. We then
investigate dynamic games where players decide to change their pseudonym one
after the other and show how this affects strategies at equilibrium. Finally, we design
protocols-PseudoGame protocols-based on the results of our analysis and simulate
their performance in vehicular network scenarios.
57. Improving Security and Performance in the Tor
Network through Tunable Path Selection.
Synopsis:
The Tor anonymous communication network uses self-reported bandwidth values to select
routers for building tunnels. Since tunnels are allocated in proportion to this bandwidth, this
allows a malicious router operator to attract tunnels for compromise. Although Tor limits the
self-reported bandwidth, it uses a high maximum value, effectively choosing performance
over high anonymity for all users. We propose a router selection algorithm that allows users
to control the trade-off between performance and anonymity. We also propose an
opportunistic bandwidth measurement algorithm to replace self-reported values that is more
sensitive to load and more responsive to changing network conditions. Our mechanism
effectively blends the traffic from users of different preferences, making partitioning attacks
difficult. We implemented the opportunistic measurement and tunable performance
extensions and examined their performance both through simulation and in the real Tor
network. Our results show that users can get dramatic increases in either performance or
anonymity with little to no sacrifice in the other metric, or a more modest improvement in
both. Our mechanisms are also invulnerable to the previously published low-resource
attacks on Tor.
58. An Approach to Detect and Prevent Tautology
Type SQL Injection in Web Service Based on
XSchema validation.

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Synopsis:
SQL injection is the most common attack for web applications and widely used attacking
method by hackers all over the world.
This is an attacking method that aims the data stored in a database through the firewall that
shield it. The poor input validation in code and
website administration leads SQL injection to attack the web resource. This proposed
system exhibit a dynamic method to detect and prevent
tautology type SQL Injection from malicious web users who wants to access any resource in
web related application. To detect malicious
attack and prevent malicious users from accessing web resources, this system uses an
effective SQL Query processing based on XML
Schema validation. This proposed system can be used in web application logging and
security. This work also concludes effectiveness and
performance of the system using the resultant data of proposed system..
59. IPAS: Implicit Password Authentication System.
Synopsis:
Authentication is the first line of defense against compromising confidentiality and integrity.
Though traditional login/password based schemes are easy to implement, they have been
subjected to several attacks. As an alternative, token and biometric based authentication
systems were introduced. However, they have not improved substantially to justify the
investment. Thus, a variation to the login/password scheme, viz. graphical scheme was
introduced. But it also suffered due to shoulder-surfing and screen dump attacks. In this paper,
we introduce a framework of our proposed (IPAS) Implicit Password Authentication System,
which is immune to the common attacks suffered by other authentication schemes.
60. Privacy Preserving Data Sharing With
Anonymous ID Assignment.
Synopsis:
An algorithm for anonymous sharing of private data among parties is developed. This technique
is used iteratively to assign these nodes ID numbers ranging from 1 to . This assignment is
anonymous in that the identities received are unknown to the other members of the group.

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Resistance to collusion among other members is verified in an information theoretic sense when
private communication channels are used. This assignment of serial numbers allows more
complex data to be shared and has applications to other problems in privacy preserving data
mining, collision avoidance in communications and distributed database access. The required
computations are distributed without using a trusted central authority. Existing and new
algorithms for assigning anonymous IDs are examined with respect to trade-offs between
communication and computational requirements. The new algorithms are built on top of a
N ’ S ’ A
for distributed solution of certain polynomials over finite fields enhances the scalability of the
algorithms. Markov chain representations are used to find statistics on the number of iterations
required, and computer algebra gives closed form results for the completion rates.
61. Online Intrusion Alert Aggregation with Generative
Data Stream Modeling.
Synopsis:
Alert aggregation is an important subtask of intrusion detection. The goal is to identify and
to cluster different alerts-produced by low-level intrusion detection systems, firewalls, etc.-
belonging to a specific attack instance which has been initiated by an attacker at a certain
point in time. Thus, meta-alerts can be generated for the clusters that contain all the
relevant information whereas the amount of data (i.e., alerts) can be reduced substantially.
Meta-alerts may then be the basis for reporting to security experts or for communication
within a distributed intrusion detection system. We propose a novel technique for online
alert aggregation which is based on a dynamic, probabilistic model of the current attack
situation. Basically, it can be regarded as a data stream version of a maximum likelihood
approach for the estimation of the model parameters. With three benchmark data sets, we
demonstrate that it is possible to achieve reduction rates of up to 99.96 percent while the
number of missing meta-alerts is extremely low. In addition, meta-alerts are generated with
a delay of typically only a few seconds after observing the first alert belonging to a new
attack instance.
62. Automatic Reconfiguration for Large-Scale Reliable
Storage Systems.
Synopsis:
Byzantine-fault-tolerant replication enhances the availability and reliability of Internet
services that store critical state and preserve it despite attacks or software errors. However,
existing Byzantine-fault-tolerant storage systems either assume a static set of replicas, or
have limitations in how they handle reconfigurations (e.g., in terms of the scalability of the

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solutions or the consistency levels they provide). This can be problematic in long-lived,
large-scale systems where system membership is likely to change during the system
lifetime. In this paper, we present a complete solution for dynamically changing system
membership in a large-scale Byzantine-fault-tolerant system. We present a service that
tracks system membership and periodically notifies other system nodes of membership
changes. The membership service runs mostly automatically, to avoid human configuration
errors; is itself Byzantine-fault-tolerant and reconfigurable; and provides applications with a
sequence of consistent views of the system membership. We demonstrate the utility of this
membership service by using it in a novel distributed hash table called dBQS that provides
atomic semantics even across changes in replica sets. dBQS is interesting in its own right
because its storage algorithms extend existing Byzantine quorum protocols to handle
changes in the replica set, and because it differs from previous DHTs by providing
Byzantine fault tolerance and offering strong semantics. We implemented the membership
service and dBQS. Our results show that the approach works well, in practice: the
membership service is able to manage a large system and the cost to change the system
membership is low.
63. Privacy-Preserving Updates to Anonymous and
Confidential Databases.
Synopsis:
Suppose Alice owns a k-anonymous database and needs to determine whether her
database, when inserted with a tuple owned by Bob, is still k-anonymous. Also, suppose
that access to the database is strictly controlled, because for example data are used for
certain experiments that need to be maintained confidential. Clearly, allowing Alice to
directly read the contents of the tuple breaks the privacy of Bob (e.g., a patient's medical
record); on the other hand, the confidentiality of the database managed by Alice is violated
once Bob has access to the contents of the database. Thus, the problem is to check
whether the database inserted with the tuple is still k-anonymous, without letting Alice and
Bob know the contents of the tuple and the database, respectively. In this paper, we
propose two protocols solving this problem on suppression-based and generalization-based
k-anonymous and confidential databases. The protocols rely on well-known cryptographic
assumptions, and we provide theoretical analyses to proof their soundness and
experimental results to illustrate their efficiency.
64. Reversible Data Hiding in Encrypted Images by
Reserving Room Before Encryption.
Synopsis:

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Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted
images, since it maintains the excellent property that the original cover can be losslessly
recovered after embedded data is extracted while protecting the image content's
confidentiality. All previous methods embed data by reversibly vacating room from the
encrypted images, which may be subject to some errors on data extraction and/or image
restoration. In this paper, we propose a novel method by reserving room before encryption
with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed
data in the encrypted image. The proposed method can achieve real reversibility, that is,
data extraction and image recovery are free of any error. Experiments show that this novel
method can embed more than 10 times as large payloads for the same image quality as the
previous methods, such as for PSNR=40 dB.
65. Replica Placement for Route Diversity in Tree-Based
Routing Distributed Hash Tables.
Synopsis:
Distributed hash tables (DHTs) share storage and routing responsibility among all nodes in
a peer-to-peer network. These networks have bounded path length unlike unstructured
networks. Unfortunately, nodes can deny access to keys or misroute lookups. We address
both of these problems through replica placement. We characterize tree-based routing
DHTs and define MaxDisjoint, a replica placement that creates route diversity for these
DHTs. We prove that this placement creates disjoint routes and find the replication degree
necessary to produce a desired number of disjoint routes. Using simulations of Pastry (a
tree-based routing DHT), we evaluate the impact of MaxDisjoint on routing robustness
compared to other placements when nodes are compromised at random or in a contiguous
run. Furthermore, we consider another route diversity mechanism that we call neighbor set
routing and show that, when used with our replica placement, it can successfully route
messages to a correct replica even with a quarter of the nodes in the system compromised
at random. Finally, we demonstrate a family of replica query strategies that can trade off
response time and system load. We present a hybrid query strategy that keeps response
time low without producing too high a load.
66. Design and Implementation of TARF: A Trust-Aware
Routing Framework for WSNs.
Synopsis:
The multihop routing in wireless sensor networks (WSNs) offers little protection against
identity deception through replaying routing information. An adversary can exploit this defect
to launch various harmful or even devastating attacks against the routing protocols,

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including sinkhole attacks, wormhole attacks, and Sybil attacks. The situation is further
aggravated by mobile and harsh network conditions. Traditional cryptographic techniques or
efforts at developing trust-aware routing protocols do not effectively address this severe
problem. To secure the WSNs against adversaries misdirecting the multihop routing, we
have designed and implemented TARF, a robust trust-aware routing framework for dynamic
WSNs. Without tight time synchronization or known geographic information, TARF provides
trustworthy and energy-efficient route. Most importantly, TARF proves effective against
those harmful attacks developed out of identity deception; the resilience of TARF is verified
through extensive evaluation with both simulation and empirical experiments on large-scale
WSNs under various scenarios including mobile and RF-shielding network conditions.
Further, we have implemented a low-overhead TARF module in TinyOS; as demonstrated,
this implementation can be incorporated into existing routing protocols with the least effort.
Based on TARF, we also demonstrated a proof-of-concept mobile target detection
application that functions well against an antidetection mechanism..
67. Robust Correlation of Encrypted Attack Traffic through
Stepping Stones by Flow Watermarking.
Synopsis:
Network-based intruders seldom attack their victims directly from their own computer. Often,
“ ”
identity and origin. To identify the source of the attack behind the stepping stone(s), it is
necessary to correlate the incoming and outgoing flows or connections of a stepping stone.
To resist attempts at correlation, the attacker may encrypt or otherwise manipulate the
connection traffic. Timing-based correlation approaches have been shown to be quite
effective in correlating encrypted connections. However, timing-based correlation
approaches are subject to timing perturbations that may be deliberately introduced by the
attacker at stepping stones. In this paper, we propose a novel watermark-based-correlation
scheme that is designed specifically to be robust against timing perturbations. Unlike most
previous timing-based correlation approaches, our watermark- “ ”
that it embeds a unique watermark into the encrypted flows by slightly adjusting the timing
of selected packets. The unique watermark that is embedded in the encrypted flow gives us
a number of advantages over passive timing-based correlation in resisting timing
perturbations by the attacker. In contrast to the existing passive correlation approaches, our
active watermark-based correlation does not make any limiting assumptions about the
distribution or random process of the original interpacket timing of the packet flow. In theory,
our watermark-based correlation can achieve arbitrarily close to 100 percent correlation true
positive rate (TPR), and arbitrarily close to 0 percent false positive rate (FPR) at the same
time for sufficiently long flows, despite arbitrarily large (but bounded) timing perturbations of
any distribution by the attacker. Our paper is the first that identifies 1) accurate quantitative
tradeoffs between the achievable correlation effectiveness - nd the defining characteristics

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of the timing perturbation; and 2) a provable upper bound on the number of packets needed
to achieve a desired correlation effectiveness, given the amount of timing perturbation.
Experimental results show that our active watermark-based correlation performs better and
requires fewer packets than existing, passive timing-based correlation methods in the
presence of random timing perturbations.
68. Secure Service-Oriented Architecture for Mobile
Transactions.
Synopsis:
The paper describes secure service-oriented architecture for mobiletransactions. The
architecture comprises components, protocols, applicationsand interfaces and it provides
various security services to various mobileapplications:Registration, Certification,
Authentication, and Authorization of users,secure messaging at an application–level (end–
to–end security), protectionof data in databases, and security services for protection of its
owncomponents. The architecture is modular, integrated, extendible andscalable. The
paper describes design of the architecture, the status of itscurrent implementation, and
future research and development plans
69. The Geometric Efficient Matching Algorithm for
Firewalls.
Synopsis:
Since firewalls need to filter all the traffic crossing the network perimeter, they should be
able to sustain a very high throughput, or risk becoming a bottleneck. Firewall packet
matching can be viewed as a point location problem: Each packet (point) has five fields
(dimensions), which need to be checked against every firewall rule in order to find the first
matching rule. Thus, algorithms from computational geometry can be applied. In this paper,
we consider a classical algorithm that we adapted to the firewall domain. We call the
“G E M ” (GEM) T GEM j
logarithmic matching time performance. However, the algorithm's theoretical worst-case
space complexity is O(n4) for a rule-base with n rules. Because of this perceived high space
complexity, GEM-like algorithms were rejected as impractical by earlier works. Contrary to
this conclusion, this paper shows that GEM is actually an excellent choice. Based on
statistics from real firewall rule-bases, we created a Perimeter rules model that generates
random, but nonuniform, rule-bases. We evaluated GEM via extensive simulation using the
Perimeter rules model. Our simulations show that on such rule-bases, GEM uses near-
linear space, and only needs approximately 13 MB of space for rule-bases of 5,000 rules.
Moreover, with use of additional space improving heuristics, we have been able to reduce

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the space requirement to 2-3 MB for 5,000 rules. But most importantly, we integrated GEM
into the code of the Linux iptables open-source firewall, and tested it on real traffic loads.
Our GEM-iptables implementation managed to filter over 30,000 packets-per-second on a
standard PC, even with 10,000 rules. Therefore, we believe that GEM is an efficient and
practical algorithm for firewall packet matching.
70. Fast Matrix Embedding by Matrix Extending.
Synopsis:
When designing steganographic schemes, matrix embedding is an efficient method for
increasing the embedding efficiency that is defined as an average number of bits
embedded via per change on the cover. Random linear code-based matrix embedding can
achieve high embedding efficiency but cost much in computation. In this paper, we propose
a method to increase the embedding speed of matrix embedding by extending the matrix
via some referential columns. Compared with the original matrix embedding, the proposed
method can exponentially reduce the computational complexity for equal increment of
embedding efficiency. Experimental results also show that this novel method achieves
higher embedding efficiency and faster embedding speed than previous fast matrix
embedding methods, and thus is more suitable for real-time steganogaphic systems.
71. Joint Relay and Jammer Selection for Secure Two-Way
Relay Networks.
Synopsis:
In this paper, we investigate joint relay and jammer selection in two-way cooperative networks,
consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the
constraints of physical-layer security. Specifically, the proposed algorithms select two or three
intermediate nodes to enhance security against the malicious eavesdropper. The first selected
node operates in the conventional relay mode and assists the sources to deliver their data to the
corresponding destinations using an amplify-and-forward protocol. The second and third nodes
are used in different communication phases as jammers in order to create intentional
interference upon the malicious eavesdropper. First, we find that in a topology where the
intermediate nodes are randomly and sparsely distributed, the proposed schemes with
cooperative jamming outperform the conventional nonjamming schemes within a certain
transmitted power regime. We also find that, in the scenario where the intermediate nodes
gather as a close cluster, the jamming schemes may be less effective than their nonjamming
counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and
nonjamming modes. Simulation results validate our theoretical analysis and show that the
hybrid switching scheme further improves the secrecy rate.

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72. Color Extended Visual Cryptography Using Error
Diffusion.
Synopsis:
Color visual cryptography (VC) encrypts a color secret message into color halftone image
shares. Previous methods in the literature show good results for black and white or gray
scale VC schemes, however, they are not sufficient to be applied directly to color shares
due to different color structures. Some methods for color visual cryptography are not
satisfactory in terms of producing either meaningless shares or meaningful shares with low
visual quality, leading to suspicion of encryption. This paper introduces the concept of visual
information pixel (VIP) synchronization and error diffusion to attain a color visual
cryptography encryption method that produces meaningful color shares with high visual
quality. VIP synchronization retains the positions of pixels carrying visual information of
original images throughout the color channels and error diffusion generates shares pleasant
to human eyes. Comparisons with previous approaches show the superior performance of
the new method.
73. HASBE: A Hierarchical Attribute-Based Solution for
Flexible and Scalable Access Control in Cloud Computing.
Synopsis:
Cloud computing has emerged as one of the most influential paradigms in the IT industry in
recent years. Since this new computing technology requires users to entrust their valuable
data to cloud providers, there have been increasing security and privacy concerns on
outsourced data. Several schemes employing attribute-based encryption (ABE) have been
proposed for access control of outsourced data in cloud computing; however, most of them
suffer from inflexibility in implementing complex access control policies. In order to realize
scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in
this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending
ciphertext-policy attribute-set-based encryption (ASBE) with a hierarchical structure of
users. The proposed scheme not only achieves scalability due to its hierarchical structure,
but also inherits flexibility and fine-grained access control in supporting compound attributes
of ASBE. In addition, HASBE employs multiple value assignments for access expiration
time to deal with user revocation more efficiently than existing schemes. We formally prove
the security of HASBE based on security of the ciphertext-policy attribute-based encryption
(CP-ABE) scheme by Bethencourt and analyze its performance and computational
complexity. We implement our scheme and show that it is both efficient and flexible in

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dealing with access control for outsourced data in cloud computing with comprehensive
experiments.
74. OAuth Web Authorization Protocol
Synopsis:
Allowing one Web service to act on our behalf with another has become increasingly
important as social Internet services such as blogs, photo sharing, and social networks
have become widely popular. OAuth, a new protocol for establishing identity management
standards across services, provides an alternative to sharing our usernames and
passwords, and exposing ourselves to attacks on our online data and identities.
75. On Privacy of Encrypted Speech Communications.
Synopsis:
Silence suppression, an essential feature of speech communications over the Internet,
saves bandwidth by disabling voice packet transmissions when silence is detected.
However, silence suppression enables an adversary to recover talk patterns from packet
timing. In this paper, we investigate privacy leakage through the silence suppression
feature. More specifically, we propose a new class of traffic analysis attacks to encrypted
speech communications with the goal of detecting speakers of encrypted speech
communications. These attacks are based on packet timing information only and the attacks
can detect speakers of speech communications made with different codecs. We evaluate
the proposed attacks with extensive experiments over different type of networks including
commercial anonymity networks and campus networks. The experiments show that the
proposed traffic analysis attacks can detect speakers of encrypted speech communications
with high accuracy based on traces of 15 minutes long on average.
76. Packet-Hiding Methods for Preventing Selective
Jamming Attacks.
Synopsis:
The open nature of the wireless medium leaves it vulnerable to intentional interference
attacks, typically referred to as jamming. This intentional interference with wireless
transmissions can be used as a launchpad for mounting Denial-of-Service attacks on
wireless networks. Typically, jamming has been addressed under an external threat model.
However, adversaries with internal knowledge of protocol specifications and network
secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this

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work, we address the problem of selective jamming attacks in wireless networks. In these
attacks, the adversary is active only for a short period of time, selectively targeting
messages of high importance. We illustrate the advantages of selective jamming in terms of
network performance degradation and adversary effort by presenting two case studies; a
selective attack on TCP and one on routing. We show that selective jamming attacks can be
launched by performing real-time packet classification at the physical layer. To mitigate
these attacks, we develop three schemes that prevent real-time packet classification by
combining cryptographic primitives with physical-layer attributes. We analyze the security of
our methods and evaluate their computational and communication overhead.
77. Recommendation Models for Open Authorization.
Synopsis:
Major online platforms such as Facebook, Google, and Twitter allow third-party applications
such as games, and productivity applications access to user online private data. Such
accesses must be authorized by users at installation time. The Open Authorization protocol
(OAuth) was introduced as a secure and efficient method for authorizing third-party
applications without releasing a user's access credentials. However, OAuth
implementations don't provide the necessary fine-grained access control, nor any
recommendations, i.e., which access control decisions are most appropriate. We propose
an extension to the OAuth 2.0 authorization that enables the provisioning of fine-grained
authorization recommendations to users when granting permissions to third-party
applications. We propose a multicriteria recommendation model that utilizes application-
based, user-based, and category-based collaborative filtering mechanisms. Our
collaborative filtering mechanisms are based on previous user decisions, and application
permission requests to enhance the privacy of the overall site's user population. We
implemented our proposed OAuth extension as a browser extension that allows users to
easily configure their privacy settings at application installation time, provides
recommendations on requested privacy permissions, and collects data regarding user
decisions. Our experiments on the collected data indicate that the proposed framework
efficiently enhanced the user awareness and privacy related to third-party application
authorizations.
78. Steganography Bit Plane Complexity Segmentation
(BPCS) Technique.
Synopsis:
Steganography is an ancient technique of data hiding. Steganography is a technique in
which secret data is hidden into vessel image without any suspicion. All other traditional

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techniques have limited data hiding capacity and can hide up to 15% of data amount of
vessel image. This paper focuses on basic steganography and various characteristics
necessary for data hiding. More importantly, the paperimplements a steganographic
technique that has hiding capacity up to 50 – 60% [8] [9]. This technique is called Bit Plane
Complexity Segmentation (BPCS) Steganography. The main principle of BPCS technique is
that, the binary image is divided into informative region and noise-like region. The secret
data is hidden into noise-like region of the vessel image without any deterioration. In our
BPCS P “E j K & R O E ”
experimented by using two images i) vessel imageof 512 x 512 size ii) secret image of 256
x 256 size. We performed this experiment for 3 different sets of images and calculated
image hiding capacity.

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