Jorge Cortés


Differential privacy in control and network systems
J. Cortés, G. E. Dullerud, S. Han, J. Le Ny, S. Mitra, G. J. Pappas
Proceedings of the IEEE Conference on Decision and Control, Las Vegas, Nevada, USA, 2016, pp. 4252-4272


As intelligent automation and large-scale distributed monitoring and control systems become more widespread, concerns are growing about the way these systems collect and make use of privacy-sensitive data obtained from individuals. This tutorial paper gives a systems and control perspective on the topic of privacy preserving data analysis, with a particular emphasis on the processing of dynamic data as well as data exchanged in networks. Specifically, we consider mechanisms enforcing differential privacy, a state-of-the-art definition of privacy initially introduced to analyze large, static datasets, and whose guarantees hold against adversaries with arbitrary side information. We discuss in particular how to perform tasks such as signal estimation, consensus and distributed optimization between multiple agents under differential privacy constraints.


Mechanical and Aerospace Engineering, University of California, San Diego
9500 Gilman Dr, La Jolla, California, 92093-0411

Ph: 1-858-822-7930
Fax: 1-858-822-3107

cortes at
Skype id: jorgilliyo