Homomorphic authentication with random masking technique ensuring privacy
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This document discusses a homomorphic authentication method utilizing a random masking technique to ensure data integrity and security in cloud computing. It introduces a Third Party Auditor (TPA) to verify data integrity without learning the content, enables batch auditing, and supports dynamic operations on data blocks. Extensive analysis shows that the proposed schemes are secure and efficient, addressing challenges associated with cloud data storage and privacy.
Homomorphic authentication with random masking technique ensuring privacy
- 1. HOMOMORPHIC AUTHENTICATION WITH RANDOM MASKING TECHNIQUE ENSURING PRIVACY & SECURITY IN CLOUD COMPUTING ABSTRACT Cloud computing may be defined as delivery of product rather than service. Cloud computing is a internet based computing which enables sharing of services. Many users place their data in the cloud. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in cloud computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. So correctness of data and security is a prime concern. This article studies the problem of ensuring the integrity and security of data storage in Cloud Computing. Security in cloud is achieved by signing the data block before sending to the cloud. Signing is performed using algorithm which is more secure compared to other algorithms. To ensure the correctness of data, we consider an external auditor called as third party auditor (TPA), on behalf of the cloud user, to verify the integrity of the data stored in the cloud. By utilizing public key based homomorphism authenticator with random masking privacy preserving public auditing can be achieved. The technique of bilinear aggregate signature is used to achieve batch auditing. Batch auditing reduces the computation overhead. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.
- 2. ALGORITHM: BLS: Boneh–Lynn–Shacham Signing is performed using Boneh–Lynn–Shacham (BLS) algorithm which is more secure compared to other algorithms. To ensure the correctness of data, we consider an external auditor called as third party auditor (TPA), on behalf of the cloud user, to verify the integrity of the data stored in the cloud. By utilizing public key based homomorphic authenticator with random masking privacy preserving public auditing can be achieved. The technique of bilinear aggregate signature is used to achieve batch auditing. Batch auditing reduces the computation overhead. Extensive security and performance analysis shows the proposed schemes are provably secure MODULES: 1. System Model: Third Party Auditor (TPA): an optional TPA, who has expertise and capabilities that users may not have, is trusted to assess and expose risk of cloud storage services on behalf of the users upon request. 1. It supports an external auditor to audit the user’s outsourced data without learning knowledge on the data content. 2. Achieves batch auditing where multiple delegated auditing asks from different users can be performed simultaneously by the TPA. 3. Also supports dynamic operations on data blocks i.e. data update, append and delete.
- 3. 2. File Retrieval and Error Recovery: Since our layout of file matrix is systematic, the user can reconstruct the original file by downloading the data vectors from the first m servers, assuming that they return the correct response values. Notice that our verification scheme is based on random spot-checking, so the storage correctness assurance is a probabilistic one. We can guarantee the successful file retrieval with high probability. On the other hand, whenever the data corruption is detected, the comparison of pre-computed tokens and received response values can guarantee the identification of misbehaving server(s). 3. Operations: (1) Update Operation In cloud data storage, sometimes the user may need to modify some data block(s) stored in the cloud, we refer this operation as data update. In other words, for all the unused tokens, the user needs to exclude every occurrence of the old data block and replace it with the new one. (2) Delete Operation Sometimes, after being stored in the cloud, certain data blocks may need to be deleted. The delete operation we are considering is a general one, in which user replaces the data block with zero or some special reserved data symbol. From this point of view, the delete operation is actually a special case of the data update operation, where the original data blocks can be replaced with zeros or some predetermined special blocks. (3) Append Operation
- 4. In some cases, the user may want to increase the size of his stored data by adding blocks at the end of the data file, which we refer as data append. We anticipate that the most frequent append operation in cloud data storage is bulk append, in which the user needs to upload a large number of blocks (not a single block) at one time. EXISTING SYSTEM: The audit from TPA demands retrieval of user’s data, which should be prohibitive because it violates the privacy-preserving guarantee. Its communication and computation complexity are both linear with respect to the sampled data size, which may result in large communication overhead and time delay, especially when the bandwidth available between the TPA and the cloud server is limited. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, Cloud Computing moves the application software and databases to the large data centers, where the management of the data and services May not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. PROPOSED SYSTEM: We consider an external auditor called as third party auditor (TPA), on behalf of the cloud user, to verify the integrity of the data stored in the cloud. By utilizing public key based homomorphic authenticator with random masking privacy preserving public auditing can be achieved. The technique of bilinear aggregate signature is used to achieve batch auditing. Batch auditing reduces
- 5. the computation overhead. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient. We are going to tackle the problem of how to enable a privacy-preserving third-party auditing protocol, independent to data encryption in this paper. Besides, with the prevalence of Cloud Computing, a foreseeable increase of auditing tasks from different users may be delegated to TPA. Technical contribution in this paper is summarized as follows: 1. It supports an external auditor to audit the user’s outsourced data without learning knowledge on the data content. 2. Achieves batch auditing where multiple delegated auditing asks from different users can be performed simultaneously by the TPA. 3. Also supports dynamic operations on data blocks i.e. data update, append and delete.