Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering
By Niklas Büscher, Spyros Boukoros, Stefan Bauregger, Stefan Katzenbeisser. 2017
49 citations recorded by the source corpus.
Abstract The widespread deployment of smart meters that frequently report energy consumption information, is a known threat to consumers’ privacy. Many promising privacy protection mechanisms based on secure aggregation schemes have been proposed. Even though these schemes are cryptographically secure, the energy provider has access to the plaintext aggregated power consumption. A privacy trade-off exists between the size of the aggregation scheme and the personal data that might be leaked, where smaller aggregation sizes leak more personal data. Recently, a UK industrial body has studied this privacy trade-off and identified that two smart meters forming an aggregate, are sufficient to achieve privacy. In this work, we challenge this study and investigate which aggregation sizes are sufficient to achieve privacy in the smart grid. Therefore, we propose a flexible, yet formal privacy metric using a cryptographic game based definition. Studying publicly-available, real world energy consumption datasets with various temporal resolutions, ranging from minutes to hourly intervals, we show that a typical household can be identified with very high probability. For example, we observe a 50% advantage over random guessing in identifying households for an aggregation size of 20 households with a 15-minutes reporting interval. Furthermore, our results indicate that single appliances can be identified with significant probability in aggregation sizes up to 10 households.
Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering
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In Artificial Intelligence
- Emiliano De Cristofaro
- Carmela Troncoso
- Apostolos Pyrgelis
- Reza Shokri
- Yang Zhang
- Congzheng Song
- Vitaly Shmatikov
- Neil Zhenqiang Gong
In Control and Systems Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “[4] recently evaluate membership inference in the context of data aggregation in smart metering, studying how many household readings need to be aggregated in order to protect privacy of individual profiles in a smart grid.”