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Analysis of the Impact of Data Granularity on Privacy for the Smart Grid

Valentin Tudor (Institutionen för data- och informationsteknik, Nätverk och system, Datakommunikation och distribuerade system (Chalmers)) ; Magnus Almgren (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Marina Papatriantafilou (Institutionen för data- och informationsteknik, Nätverk och system, Datakommunikation och distribuerade system (Chalmers))
1st ACM Workshop on Language Support for Privacy-Enhancing Technologies, PETShop 2013 - Co-located with the 20th ACM Conference on Computer and Communications Security, CCS 2013; Berlin; Germany; 4 November 2013 through 4 November 2013 (1543-7221). p. 61-70. (2013)
[Konferensbidrag, refereegranskat]

The upgrade of the electricity network to the "smart grid" has been intensified in the last years. The new automated devices being deployed gather large quantities of data that offer promises of a more resilient grid but also raise privacy concerns among customers and energy distributors. In this paper, we focus on the energy consumption traces that smart meters generate and especially on the risk of being able to identify individual customers given a large dataset of these traces. This is a question raised in the related literature and an important privacy research topic. We present an overview of the current research regarding privacy in the Advanced Metering Infrastructure. We make a formalization of the problem of de-anonymization by matching low-frequency and high-frequency smart metering datasets and we also build a threat model related to this problem. Finally, we investigate the characteristics of these datasets in order to make them more resilient to the de-anonymization process. Our methodology can be used by electricity companies to better understand the properties of their smart metering datasets and the conditions under which such datasets can be released to third parties.

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Denna post skapades 2013-12-10. Senast ändrad 2016-07-05.
CPL Pubid: 188789


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