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 publiclyavailable, 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.

Abstract. Software testing is still the most established and scalable quality-assurance technique in practice. However, generating effective test suites remains computationally expensive, consisting of repetitive reachability analyses for multiple test goals according to a coverage criterion. This situation is even worse when testing entire software product lines, i.e., families of similar program variants, requiring a sufficient coverage of all derivable program variants. Instead of considering every product variant one-by-one, family-based approaches are variabilityaware analysis techniques in that they systematically explore similarities among the different variants. Based on this principle, we present a novel approach for automated product-line test-suite generation incorporating extensive reuse of reachability information among test cases derived for different test goals and/or program variants. We present a tool implementation on top of CPA/tiger which is based on CPAchecker, and provide evaluation results obtained from various experiments, revealing a considerable increase in efficiency compared to existing techniques.