Error-Resilient Masking Approaches for Privacy Preserving Data Aggregation

The deployment of future energy systems promises a number of advantages for a more stable and reliable grid as well as for a sustainable usage of energy resources. The efficiency and effectiveness of such smart grids rely on customer consumption data that is collected, processed, and analyzed. This data is used for billing, monitoring, and prediction. However, this implies privacy threats. Approaches exist that aim to either encrypt data in certain ways, to reduce the resolution of data or to mask data in a way so that an individuals' contribution is untraceable. While the latter is an effective way for protecting customer privacy when aggregating over space or time, one of the drawbacks of these approaches is the limitation or full negligence of device failures. In this paper, we therefore propose a masking approach for spatio-temporal aggregation of time series for protecting individual privacy while still providing sufficient error-resilience and reliability.