A Decentralized Mechanism Based on Differential Privacy for Privacy-Preserving Computation in Smart Grid

Zhigao Zheng, Tao Wang, Ali Kashif Bashir, Mamoun Alazab, Shahid Mumtaz, Xiaoyan Wang

Research output: Contribution to journalArticlepeer-review

Abstract

As one of most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. It suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering measurements. However, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode information of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis.

Original languageEnglish
JournalIEEE Transactions on Computers
Early online dateNov 2021
DOIs
Publication statusE-pub ahead of print - Nov 2021

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