Towards Realistic Smart Meter Privacy against Bayesian Inference

Sammanfattning: Smart meters, now an essential component of modern power grids, allow energy providers to remotely monitor users' energy consumption in near real-time. While this technology offers numerous advantages for energy management and system efficiency, it also poses significant privacy concerns. High-resolution energy consumption data can reveal sensitive information about users' routines and activities, thus potentially jeopardizing their privacy. In particular, research has demonstrated that Bayesian inference attacks can effectively disaggregate smart meter data to deduce household appliance states and subsequently obtain sensitive user information.This thesis investigates the use of energy storage systems to protect smart meter data privacy against Bayesian inference attacks. Although several methods have been proposed in the literature that employ energy storage systems for this purpose, many rely on ideal assumptions such as lossless energy storage systems. To address this issue, a data-driven energy storage model that considers energy losses and capacity degradation has been proposed. Privacy leakage is quantified in terms of Bayesian risk, and control strategies are devised to minimize Bayesian risk while accounting for the energy storage system's operational constraints and economic implications. The findings reveal that non-idealities in energy storage systems significantly affect the privacy-preserving performance of control strategies. Moreover, incorporating degradation losses in the design of privacy-enhancing control strategies considerably improves battery life, albeit with some privacy loss.Taking into account the non-idealities of energy storage, this thesis introduces novel privacy-preserving control strategies using various adversarial models, which are classified based on their knowledge of the control system. These models include controller-aware and controller-unaware adversaries employing sequential hypothesis testing or maximum a posteriori detection. The proposed control strategies are evaluated through numerical simulations using real data and emulated energy storage systems. Additionally, the thesis provides a reference dataset of appliance power consumption, featuring detailed electrical measurements to support future smart meter privacy research. In summary, this work offers valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with potential applications extending to other sensor networks beyond smart meters.