A secure and efficient data deduplication framework for the internet of things via edge computing and blockchain. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Wu, Zeng; Huang, Hui; Zhou, Yuping; Wu, Chenhuang
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 1999
• Page End: 2025
• Publication Date: 2022-12-31
• Subjects: Data deduplication -- blockchain -- internet of things -- edge computing -- data security; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2090514
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Data deduplication can solve the problem of resource wastage caused by duplicated data. However, due to the limited resources of Internet of Things (IoT) devices, applying data deduplication to IoT scenarios is challenging. Existing data deduplication frameworks for the IoT are prone to inefficiency or trust crises due to the random allocation of edge computing nodes. Furthermore, side-channel attacks remain a risk. In addition, after IoT devices store data in the cloud through data deduplication, they cannot share their data efficiently. In this paper, we propose a secure and efficient data deduplication framework for the IoT based on edge computing and blockchain technologies. In this scheme, we propose a model based on parallel use of three-layer and two-layer architectures and introduce the RAndom REsponse (RARE) scheme to resist side-channel attacks. We also design a label tree to realise one-to-many data-sharing, which improves efficiency and meets the needs of the IoT. In addition, we use blockchain to resist collusion attacks. Experiments were conducted to demonstrate that our framework has advantages over similar schemes in terms of communication cost, security and efficiency.