Biblio

Cyber-physical systems are subject to natural uncertainties and sensor noise that can be amplified/attenuated due to feedback. In this work, we want to leverage these properties in order to define the inherent differential privacy of feedback-control systems without the addition of an external differential privacy noise. If larger levels of privacy are required, we introduce a methodology to add an external differential privacy mechanism that injects the minimum amount of noise that is needed. On the other hand, we show how the combination of inherent and external noise affects system security in terms of the impact that integrity attacks can impose over the system while remaining undetected. We formulate a bilevel optimization problem to redesign the control parameters in order to minimize the attack impact for a desired level of inherent privacy.

As smart meters continue to be deployed around the world collecting unprecedented levels of fine-grained data about consumers, we need to find mechanisms that are fair to both, (1) the electric utility who needs the data to improve their operations, and (2) the consumer who has a valuation of privacy but at the same time benefits from sharing consumption data. In this paper we address this problem by proposing privacy contracts between electric utilities and consumers with the goal of maximizing the social welfare of both. Our mathematical model designs an optimization problem between a population of users that have different valuations on privacy and the costs of operation by the utility. We then show how contracts can change depending on the probability of a privacy breach. This line of research can help inform not only current but also future smart meter collection practices.