Privacy-Aware Control of Cyber Physical Systems

Abstract

Networked Unmanned Aerial Vehicles (UAVs) can be used for complex tasks such as surveillance and reconnaissance, inspection of dangerous environments, and target pursuit. Typically, coordination between multiple UAVs has been shown to improve the ability and performance of accomplishing such tasks. However, networked UAVs introduce new vulnerabilities enabling cyber-attacks which can target critical elements and prevent the UAVs from achieving their goal. This chapter presents a distributed system architecture for coordination of UAVs that provides resilience against denial-of-service and integrity cyber-attacks. The developed architecture consists of a distributed ledger implementing an asynchronous Byzantine fault tolerant protocol exchanging data between distributed agents and a distributed learning algorithm based on vector consensus implemented on top of the distributed ledger. Performance and resilience of the architecture are evaluated using a target pursuit case study based on a hardware-in-the-loop testbed. The experimental results demonstrate that that the UAVs that are not under attack are still able to successfully cooperate and accomplish the desired task. [160 words] privacy risks to users. If left unaddressed, potential privacy risks may deter users from contributing their data to cyber-physical systems, thereby limiting the effectiveness of data-driven tools employed by the systems. To ensure that users are protected from privacy risks and feel comfortable sharing their data with cyber-physical systems, this chapter discusses how differential privacy and cryptography, techniques originally developed for privacy in health care and computer systems respectively, can be used to conduct data analysis and optimization with principled privacy guarantees in cyber-physical systems. Along the way, we will discuss and compare the properties, strengths and weaknesses of these approaches. We will also show why these techniques address many of the privacy-related issues that arise when using data anonymization and data aggregation, two of the most common approaches to privacy-aware data sharing.