Name
Abstract | ||
|---|---|---|
The widespread deployment of smart meters that frequently report energy consumption information, is a known threat to consumers’ privacy. Manypromising privacy protection mechanisms based on secure aggregation schemes have been proposed. Eventhough these schemes are cryptographically secure, theenergy provider has access to the plaintext aggregatedpower consumption. A privacy trade-off exists betweenthe size of the aggregation scheme and the personaldata that might be leaked, where smaller aggregationsizes leak more personal data. Recently, a UK industrialbody has studied this privacy trade-off and identifiedthat two smart meters forming an aggregate, are sufficient to achieve privacy. In this work, we challenge thisstudy and investigate which aggregation sizes are sufficient to achieve privacy in the smart grid. Therefore,we propose a flexible, yet formal privacy metric using acryptographic game based definition. Studying publiclyavailable, real world energy consumption datasets withvarious temporal resolutions, ranging from minutes tohourly intervals, we show that a typical household canbe identified with very high probability. For example,we observe a 50% advantage over random guessing inidentifying households for an aggregation size of 20households with a 15-minutes reporting interval. Furthermore, our results indicate that single appliances canbe identified with significant probability in aggregationsizes up to 10 households. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1515/popets-2017-0045 | PoPETs |
DocType | Volume | Issue |
Journal | 2017 | 4 |
Citations | PageRank | References |
5 | 0.51 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niklas Büscher | 1 | 47 | 4.58 |
| Spyros Boukoros | 2 | 7 | 2.91 |
| Stefan Bauregger | 3 | 17 | 1.32 |
| Stefan Katzenbeisser | 4 | 1844 | 143.68 |