Paper Info
Title
A Decentralized Mechanism Based on Differential Privacy for Privacy-Preserving Computation in Smart Grid
Abstract
As one of the 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. Unfortunately, it always 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 masurements. Moreover, 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 informaton 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.
Year
DOI
Venue
2022
10.1109/TC.2021.3130402
IEEE Transactions on Computers
Keywords
DocType
Volume
Smart gird,privacy-preserving computation,random permutation,differential privacy,non-intrusive load monitoring
Journal
71
Issue
ISSN
Citations 
11
0018-9340
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Zhigao Zheng100.34
Tao Wang2337115.68
Ali Kashif Bashir342.41
Mamoun Alazab400.34
Shahid Mumtaz5878110.36
Xiaoyan Wang600.34