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ElecPrivacy: Evaluating the Privacy Protection of Electricity Management Algorithms

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5 Author(s)
Kalogridis, G. ; Telecommun. Res. Lab., Toshiba Res. Eur. Ltd., Bristol, UK ; Cepeda, R. ; Denic, S.Z. ; Lewis, T.
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The data collected by a home smart meter can potentially reveal sensitive private information about the home resident(s). In this paper, we study how home energy resources can be used to protect the privacy of the collected data. In particular we: a) introduce a power mixing algorithm to selectively protect a set of consumption events; b) develop a range of different privacy protection metrics; c) analyze real smart metering data sampled twice a minute over a period of 13 days; and d) evaluate the protection offered by different power mixing algorithms. Major factors which determine the efficiency of the proposed power mixing algorithms are identified, such as battery capacity and power, and user preferences for privacy-based allocations of battery energy quotas.

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Smart Grid, IEEE Transactions on  (Volume:2 ,  Issue: 4 )