Secure similarity-based cloud data deduplication in Ubiquitous city

Abstract

Ubiquitous city, a wonderful vision of future urban, enables citizens to access to any infrastructure and enjoy high quality urban services by integrating information and communication technologies into urban management. However, it inevitably brings a huge amount of data in the Ubiquitous city scenario. It makes how to efficiently manage the ever-increasing datum while preserving data privacy a challenge task. To cope with the above issue, secure data deduplication has attracted considerable interests both academic and industrial community. It can reduce the amount of storage cost by eliminating duplicate data copies, while providing data privacy. Although message-locked encryption has been widely adopted to perform secure cross-client deduplication, it will bring many additional metadata located both client and cloud sides. Recently, some researchers proposed a novel extension of message-locked encryption, named block-level message-locked encryption (BL-MLE), in which block keys are encapsulated into block tags to save metadata storage space. We argue that BL-MLE suffers from high computation overhead for block tag comparison, especially in dissimilar files setting. In this paper, we propose a novel secure similarity-based data deduplication scheme by integrating the technologies of bloom filter and content-defined chunking, which can significantly reduce the computation overhead by only performing deduplication operations for similar files. Security and efficiency evaluations show that the proposed scheme can achieve the desired security goals, while providing a comparable computation overhead.