FiDD: Secure Fine-Grained Deduplication and Dynamic Auditing Scheme for Cloud Storage

Longxia Huang, Xue Mao, Lei Zhou, Di Wu, Longxiang Gao, Tom H. Luan

Abstract: With the rapid development of cloud computing, more and more users tend to store their data remotely to the cloud. Taking into account data security and resource utilization comprehensively, in addition to providing users with basic remote data integrity verification, cloud servers also need to conduct redundancy checks. However, current deduplication schemes primarily focus on static file-level data and auditing processes, rendering them inadequate for managing resources with dynamic attributes. In this paper, we propose a fine-grained deduplication and dynamic auditing model (FiDD) for cloud storage to address these challenges. FiDD utilizes homomorphic verifier-based data tags to seamlessly integrate deduplication and auditing processes, allowing both block-level and file-level deduplications. Additionally, FiDD employs doubly linked lists and multi-set hash functions to enhance the efficiency of data updates. The security of FiDD is validated through rigorous security proofs, while its efficiency is demonstrated through comprehensive experimental analysis. The experiments demonstrate an average improvement of at least 35% in audit efficiency and at least 50% in dynamics efficiency. Consequently, FiDD enhanes the security of data management in cloud computing while improving its overall efficiency.

External IDs:dblp:journals/ton/HuangMZWGL26