Fuzzy Identity-Based Dynamic Auditing of Big Data on Cloud Storage

Abstract

To ensure the reliability and integrity of data in the cloud storage server, some scholars provided various data integrity auditing schemes. However, the most existing data integrity auditing schemes only support the static data and may be unsuitable for the dynamic operations of data. To overcome this difficulty, we propose a fuzzy identity-based dynamic auditing of big data, which combines the structure of the Merkle hash tree (MHT) with the Index logic table (ILT). Our scheme not only performs the dynamic operations of data block in the ILT, namely modification, insertion and deletion, but also efficiently executes dynamic operations of the ILT on the structure of the MHT. We also elaborate the security, characteristics and performance analysis of the proposed scheme separately. The analysis results show that the proposed scheme costs less time than the structure of the original MHT to generate the root node hash value during the metadata generation phase and update the root node hash value during the dynamic operations. Furthermore, when users store the new ILT in local storage, they require lower communication cost to update root node hash value than users without storing the ILT, and fewer interactions between the cloud storage server and users in the dynamic operations process.