Privacy-Preserving Deduplication of Sensor Compressed Data in Distributed Fog Computing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Related Research Unit(s)


Original languageEnglish
Number of pages17
Journal / PublicationIEEE Transactions on Parallel and Distributed Systems
Publication statusOnline published - 3 Jun 2022


Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.

Research Area(s)

  • Similar data deduplication, fog-cloud network, brute-force attacks, frequency analysis, message equality information