NDLI: A Randomized Response Model for Privacy Preserving Smart Metering

Content Provider	IEEE Xplore Digital Library
Author	Shuang Wang Lijuan Cui Jialan Que Dae-Hyun Choi Xiaoqian Jiang Cheng, S. Le Xie
Copyright Year	2010
Abstract	The adoption of smart meters may bring new privacy concerns to the general public. Given the fact that metering data of individual homes/factories is accumulated every 15 min, it is possible to infer the pattern of electricity consumption of individual users. In order to protect the privacy of users in a completely de-centralized setting (i.e., individuals do not communicate with one another), we propose a novel protocol, which allows individual meters to report the true electricity consumption reading with a pre-determined probability. Load serving entities (LSE) can reconstruct the total electricity consumption of a region or a district through inference algorithm, but their ability of identifying individual users' energy consumption pattern is significantly reduced. Using simulated data, we verify the feasibility of the proposed method and demonstrate performance advantages over existing approaches.
Starting Page	1317
Ending Page	1324
Page Count	8
File Size	1218774
File Format	PDF
ISSN	19493053
Volume Number	3
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Privacy Electricity Estimation Meter reading Inference algorithms Accuracy Approximation methods smart metering Data privacy Gaussian mixture
Content Type	Text
Resource Type	Article
Subject	Computer Science

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