Differentially Private Linear-Quadratic Control


Differentially Private Linear-Quadratic Control
Tuesday, January 22, 2019
11AM – 12PM
POB 6.304

Matthew Hale

As multi-agent systems grow and become increasingly data-driven, more and more personal data can be shared with unknown or unintended recipients. For example, self-driving cars may share position information for collision avoidance, and smart power grids may share power consumption data to optimize power generation. Even seemingly innocuous data can be very revealing about users, and new data-driven technologies must therefore protect sensitive user data while still allowing networks of agents to function. To address this need, I will present a differentially private implementation for multi-agent tracking control. This talk will use the classic linear-quadratic (LQ) tracking problem to give a broadly applicable problem formulation, and I will cover a recent privacy implementation that integrates a centralized cloud computer into an otherwise decentralized network. The agents add noise to all data sent to the cloud in order to enforce differential privacy, which gives each agent strong, rigorous privacy guarantees. In contrast to some existing approaches, the cloud does not need to be trusted and instead receives only private information from users, which it then uses to generate control values for them. Functions of private data are therefore fed back into the system. To characterize privacy in feedback, I will present numerical bounds on how difficult it is to compute control values using private user data. The end result of this work is a privacy implementation coupled with a method for quantitatively trading off individual privacy and aggregate performance in networks.

Matthew Hale is an Assistant Professor of Mechanical and Aerospace Engineering at the University of Florida. He received his BSE in Electrical Engineering from the University of Pennsylvania in 2012, and his MS and PhD in Electrical and Computer Engineering from the Georgia Institute of Technology in 2015 and 2017, respectively. His research broadly pertains to designing coordination strategies for multi-agent systems under challenging conditions. Current research interests include privacy in control, asynchronous coordination of networks, and graph theory. He directs the Control, Optimization, and Robotics Engineering (CORE) Lab at the University of Florida, which houses a swarm robotics testbed for testing and validating algorithms developed by his group.

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