A Decisional Attack to Privacy-friendly Data Aggregation in Smart Grids

The privacy-preserving management of energy consumption measurements gathered by Smart Meters plays a pivotal role in the Automatic Metering Infrastructure of Smart Grids. Grid users and standardization committees are requiring that utilities and third parties collecting aggregated metering data are prevented from accessing measurements at the household granularity, and data perturbation is a technique used to provide a trade-off between the privacy of individual users and the precision of the aggregated measurements. In this paper, we discuss a decisional attack to aggregation with data-perturbation, showing that a curious entity can exploit the temporal correlation of Smart Grid measurements to detect the presence or absence of individual data generated by a given user inside an aggregate. We also propose a countermeasure to such attack and show its effectiveness using both synthetic and real home energy consumption measurement traces.