Privacy-by-Design for Cyber-Physical Systems

It is envisioned that future cyber-physical systems will provide a more convenient living and working environment. However, such systems need inevitably to collect and process privacy-sensitive information. That means the benefits come with potential privacy leakage risks. Nowadays, this privacy issue receives more attention as a legal requirement of the EU General Data Protection Regulation. In this thesis, privacy-by-design approaches are studied where privacy enhancement is realized through taking privacy into account in the physical layer design. This work focuses in particular on cyber-physical systems namely sensor networks and smart grids. Physical-layer performance and privacy leakage risk are assessed by hypothesis testing measures. First, a sensor network in the presence of an informed eavesdropper is considered. Extended from the traditional hypothesis testing problems, novel privacy-preserving distributed hypothesis testing problems are formulated. The optimality of deterministic likelihood-based test is discussed. It is shown that the optimality of deterministic likelihood-based test does not always hold for an intercepted remote decision maker and an optimal randomized decision strategy is completely characterized by the privacy-preserving condition. These characteristics are helpful to simplify the person-by-person optimization algorithms to design optimal privacy-preserving hypothesis testing networks. Smart meter privacy becomes a significant issue in the development of smart grid technology. An innovative scheme is to exploit renewable energy supplies or an energy storage at a consumer to manipulate meter readings from actual energy demands to enhance the privacy. Based on proposed asymptotic hypothesis testing measures of privacy leakage, it is shown that the optimal privacy-preserving performance can be characterized by a Kullback-Leibler divergence rate or a Chernoff information rate in the presence of renewable energy supplies. When an energy storage is used, its finite capacity introduces memory in the smart meter system. It is shown that the design of an optimal energy management policy can be cast to a belief state Markov decision process framework.QC 20170815