Trace Recovery: Attacking and Defending the User Privacy in Smart Meter Data Analytics

Abstract

Energy consumption data is collected the service providers and shared with various stakeholders involved in a smart grid ecosystem. The fine-grained energy consumption data is immensely useful for maintaining and operating grid services. Further, these data can be used for future consumption prediction using machine learning and statistical models and market segmentation purposes. However, sharing and releasing fine-grained energy data or releasing predictive models trained on user-specific data induce explicit violations of private information of consumers [34, 41]. Thus, the service providers may share and release aggregated statistics to protect the privacy of users aiming at mitigating the privacy risks of individual users’ consumption traces. In this chapter, we show that an attacker can recover individual users’ traces of energy consumption data by exploiting regularity and uniqueness properties of individual consumption load patterns. We propose an unsupervised attack framework to recover hourly energy consumption time-series of users without any background information. We construct the problem of assigning aggregated energy consumption meter readings to individual users as a mathematical assignment problem and solve it by the Hungarian algorithm [30, 50]. We used two real-world datasets to demonstrate an attacker’s performance in recovering private traits of users. Our results show that an attacker is capable of recovering 70% of users’ energy consumption patterns with over 90% accuracy. Finally, we proposed few defense techniques, such as differential privacy and federated machine learning that may potentially help reduce an attacker’s capability to infer users’ private information.