Abstract

Individuals sharing information can improve the cost or performance of a distributed control system. But, sharing may also violate privacy. We develop a general framework for studying the cost of differential privacy in systems where a collection of agents, with coupled dynamics, communicate for sensing their shared environment while pursuing individual preferences. First, we propose a communication strategy that relies on adding carefully chosen random noise to agent states and show that it preserves differential privacy. Of course, the higher the standard deviation of the noise, the higher the cost of privacy. For linear distributed control systems with quadratic cost functions, the standard deviation becomes independent of the number agents and it decays with the maximum eigenvalue of the dynamics matrix. Furthermore, for stable dynamics, the noise to be added is independent of the number of agents as well as the time horizon up to which privacy is desired. Finally, we show that the cost of ε-differential privacy up to time T, for a linear stable system with N agents, is upper bounded by O(T3/Nε2 ).

Original languageEnglish (US)
Pages105-114
Number of pages10
DOIs
StatePublished - 2014
Event2014 3rd ACM International Conference on High Confidence Networked Systems, HiCoNS 2014, Part of CPSWeek 2014 - Berlin, Germany
Duration: Apr 15 2014Apr 17 2014

Other

Other2014 3rd ACM International Conference on High Confidence Networked Systems, HiCoNS 2014, Part of CPSWeek 2014
Country/TerritoryGermany
CityBerlin
Period4/15/144/17/14

Keywords

  • Cyber-physical security
  • Differential privacy
  • Distributed control

ASJC Scopus subject areas

  • Computer Networks and Communications

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