Integrity Checking Mechanism for PrivacyPreserved Auditing of Cloud Shared-Data

Abstract

As the cloud computing and mass data sharing develop, data integrity and privacy has become an imperativeissue. Conventional remote data auditing techniques tend to reveal sensitive data or they have high computational cost.In order to overcome these shortcomings, the Fully Homomorphic Encryption enhanced Remote Method Invocation(FHEbRMI) mechanism that includes a combination of the Modified Least Squares (MLS) optimization model and theproposed cloud auditing security and efficiency are proposed in this paper. The suggested system provides an encrypteddata auditing system, which involves RMI-based communication, to enable the client, server, and third-party auditor toperform their verification functions remotely without the disclosure of the plaintext data. An actual execution of thesuggested structure is introduced, such as secure key generation, trapdoor-based dimensionality reduction, ciphertextmultiplication, and optimized homomorphic functions. Moreover, the RMI interface provides a smooth communicationamong the distributed nodes and increases the scalability and minimizes transmission delays. A comparative study withthe recent homomorphic-based auditing schemes like blockchain-assisted, certificateless and lattice-based FHE modelreveals that the proposed FHEbRMI-MLS model has better performance in terms of encryption/decryption latency,computational cost, and encryption overhead. The experimental performance is indicative of an average 37 and 42factor in speed of encryption and enhancement of computational efficiency respectively with respect to the traditionalFHE models. This paper presents a viable, privacy-friendly auditing framework of clouds which guarantees the end-toend encrypted verification without sacrificing the efficiency.