Visible to the public CPS: Medium: Distorting the adversary's view: a CPS approach to privacy and securityConflict Detection Enabled

Project Details
Lead PI:Christina Fragouli
Co-PI(s):Paulo Tabuada
Suhas Diggavi
Performance Period:09/01/17 - 08/31/20
Institution(s):University of California-Los Angeles
Sponsor(s):National Science Foundation
Award Number:1740047
125 Reads. Placed 480 out of 803 NSF CPS Projects based on total reads on all related artifacts.
Abstract: This project develops a novel Cyber Physical System (CPS) centric approach to privacy and security for wireless networked CPS systems, by reconciling the low-delay and low-jitter requirements of CPS applications with the requirements imposed by security and privacy. Our starting observation is that, in CPS, an adversary's primary goal is not to learn all the raw data, but instead core attributes, such as the state or control actions that are derived from data. Building on this observation, we propose to use a distortion measure for security that maximizes the difference between the eavesdropper's estimate and the true value of the function computing the attributes of interest, reducing the adversary's ability to disrupt normal operation of CPS. We posit that we can protect these core attributes with fewer resources than needed to protect all the raw data. Ensuring secure and private information exchange over networked CPS systems is essential to building a thriving ecosystem of applications that range from autonomous cars and drones, to the Internet-of-Things (IoT), to immersive environments such as augmented reality for health, education, and collaboration. Our educational plan engages not only graduate students and postdocs but also high school and undergraduate students. It also reaches out to engineers and the lay public, by providing open source implementations of our algorithms making them available both to industry and hobbyists. The project considers both passive and active attacks. We will quantify novel privacy and security measures for CPS systems that are based on distortion measurements in a metric space; we will develop fundamental bounds as well as low complexity and low overhead coding schemes; we will quantify the disruptive power of active adversaries and design pro-active and retro-active defense mechanisms; and we will illustrate our approach over a flagship application, drone localization. Our approach will offer an alternative to wireless network encryption methods, by designing for low-delay, low-jitter requirements of CPS.