An efficient secure data deduplication method using radix trie with bloom filter (SDD-RT-BF) in cloud environment

Abstract

Recent advancements in the domain of cloud computing (CC) and big data technologies leads to an exponential increase in cloud data, huge replica data utilized the available memory space and maximum computation brought a major issue to the restricted cloud storage space. This paper develops an effective radix trie (RT) with Bloom Filter (BF) based secure data deduplication model, abbreviated as SDD-RT-BD. The proposed SDD-RT-BF model involves three major stages namely, authorized deduplication, proof of ownership and role key update. Initially, a convergent encryption approach is applied for preventing the leakage of data and employed role re-encryption process for attaining authorized deduplication resourcefully. Specifically, management centre handles the authorized request, and establish a RT structure to map the relationship among roles and keys. Besides, BF is applied for the implementation of data updating and enhance the retrieval of ownership verifying efficiently. The inclusion of RT along with BF for secure data deduplication shows the novelty of the paper. A detailed simulation experiments takes place for demonstrating the security and effectiveness of the presented model. The experimental outcome pointed out that the SDD-RT-BF model possesses many beneficial features namely Client-side deduplication, Tag consistency preservation, Update of outsourced data and Fault tolerance. The experimental results denoted that under the file size of 8 MB, the SDD-RT-BF model offers maximum deduplication rate of 25.40% whereas the SS, SSIMI and SDM models attains minimum deduplication rate of 24.60%, 23.60% and 22.30% respectively.