Monitoring Data Consistency in Trustworthy Cloud Memory Platforms Using Fuzzy Identities

Abstract

Data integrity has been recognised as an important aspect of secure cloud storage. An auditor may quickly and easily verify the correctness of the outsourced data without having to download the data itself, thanks to data auditing protocols. Existing designs of data auditing methods provide a significant research challenge due to the complexity in key management. The purpose of this study is to tackle the intricate Fuzzy identity-based auditing is introduced, solving a significant management difficulty in cloud data integrity verification, and is the first method of its kind. To be more precise, we introduce the foundational concept of fuzzy identity-based data auditing, in which the identity of a user is conceptualised as a collection of descriptors. For this novel primitive, we formally define both the system and security models. Using biometrics as the fuzzy identity, we then demonstrate a practical implementation of an auditing system based on fuzzy identities. To provide error-tolerance, the new protocol associates a private key with one identity and uses that identity to validate the accuracy of a response created with another identity, provided both identities are sufficiently similar. With the use of the computational Diffie-Hellman assumption and the discrete logarithm assumption from the selective-ID security model, we demonstrate the safety of our protocol. At last, we create a working prototype of the protocol to show how our plan might work in practise.