Physical Layer Methods for Privacy Provision in Distributed Control and Inference

Distributed control, decision and inference schemes are ubiquitous in many current technological systems ranging from sensor networks, collaborative teams of humans and robots, and information retrieval systems. Privacy, both location and identity, is critical for many of these systems and applications. The principal thesis investigated in this paper is that the utilization of physical layer methods and implementation techniques substantially strengthens privacy in the associated algorithms and systems. In fact it is argued that without the utilization of such physical layer methods it may be expensive to have provable levels of security in these systems. We analyze the performance of such physical layer techniques. We then utilize these techniques to provide provable privacy in distributed control, decision and inference algorithms. We demonstrate the results in context of distributed Kalman filtering. We develop useful metrics to measure privacy in these distributed systems. We investigate quantitatively the effects of privacy loss on the performance of the systems.