Differentially Private Publication of Electricity Time Series Data in Smart Grids
This addresses privacy concerns for smart grid data publication, enabling safer use in energy policy and resource planning, though it is incremental as it builds on differential privacy techniques.
The paper tackled the problem of publishing electricity consumption time series data from smart grids while preserving privacy, introducing the STPT method that uses RNNs and partitioning to capture spatio-temporal patterns, and demonstrated it significantly outperforms existing benchmarks in balancing utility and privacy.
Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In particular, time-series of power consumption over geographical areas are essential in deciding the optimal placement of expensive resources (e.g., transformers, storage elements) and their activation schedules. However, publication of such data raises significant privacy issues, as it may reveal sensitive details about personal habits and lifestyles. Differential privacy (DP) is well-suited for sanitization of individual data, but current DP techniques for time series lead to significant loss in utility, due to the existence of temporal correlation between data readings. We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs. Additionally, it employs a partitioning method for releasing electricity consumption time series based on identified patterns. We demonstrate through extensive experiments, on both real-world and synthetic datasets, that STPT significantly outperforms existing benchmarks, providing a well-balanced trade-off between data utility and user privacy.