Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Differentially private
average consensus with optimal noise selection
E. Nozari, P. Tallapragada, J. Cortés
IFAC Workshop on
Distributed Estimation and Control in Networked Systems, Philadelphia,
Pennsylvania, USA, 2015, vol. 48, issue 22, pp. 203-208
Abstract
This paper studies the problem of privacy-preserving average
consensus in multi-agent systems. The network objective is to
compute the average of the initial agent states while keeping
these values differentially private against an adversary that has
access to all inter-agent messages. We establish an impossibility
result that shows that exact average consensus cannot be achieved
by any algorithm that preserves differential privacy. This result
motives our design of a differentially private discrete-time
distributed algorithm that corrupts messages with Laplacian noise
and is guaranteed to achieve average consensus in expectation. We
examine how to optimally select the noise parameters in order to
minimize the variance of the network convergence point for a
desired level of privacy.
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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 ucsd.edu
Skype id:
jorgilliyo