Differentially Private Publication of Smart Electricity Grid Data

Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In recent years, new trends have emerged towards an increase in renewable energy sources and the development of open energy markets. In this context, capturing and sharing time-series of power consumption over geographical areas are essential in deciding the optimal placement of grid components (e.g., mobile batteries and charging stations) and their activation schedules. However, doing so 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 techniques for time series are not designed to capture geospatial features, and also lead to significant loss in utility, due to their inability to effectively support sequences of readings. We introduce 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.