Differential Privacy of Populations in Routing Games
Roy Dong

Citation
Roy Dong. "Differential Privacy of Populations in Routing Games". Talk or presentation, 28, May, 2015.

Abstract
As our ground transportation infrastructure modernizes, the large amount of data being measured, transmitted, and stored motivates an analysis of the privacy aspect of these emerging cyber-physical technologies. In this paper, we consider privacy in the routing game, where the origins and destinations of drivers are considered private. This is motivated by the fact that this spatiotemporal information can easily be used as the basis for inferences for a person's activities. More specifically, we consider the differential privacy of the mapping from the amount of flow for each origin-destination pair to the traffic flow measurements on each link of a traffic network. We use a stochastic online learning framework for the population dynamics, which is known to converge to the Nash equilibrium of the routing game. We analyze the sensitivity of this process and provide theoretical guarantees on the convergence rates as well as differential privacy values for these models. We confirm these with simulations on a small example.

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Citation formats

• HTML

  Roy Dong. <a
  href="http://www.cps-forces.org/pubs/75.html"
  ><i>Differential Privacy of Populations in Routing
  Games</i></a>, Talk or presentation,  28, May,
  2015.

• Plain text

  Roy Dong. "Differential Privacy of Populations in
  Routing Games". Talk or presentation,  28, May, 2015.

• BibTeX

  @presentation{Dong15_DifferentialPrivacyOfPopulationsInRoutingGames,
      author = {Roy Dong},
      title = {Differential Privacy of Populations in Routing
                Games},
      day = {28},
      month = {May},
      year = {2015},
      abstract = {As our ground transportation infrastructure
                modernizes, the large amount of data being
                measured, transmitted, and stored motivates an
                analysis of the privacy aspect of these emerging
                cyber-physical technologies. In this paper, we
                consider privacy in the routing game, where the
                origins and destinations of drivers are considered
                private. This is motivated by the fact that this
                spatiotemporal information can easily be used as
                the basis for inferences for a person's
                activities. More specifically, we consider the
                differential privacy of the mapping from the
                amount of flow for each origin-destination pair to
                the traffic flow measurements on each link of a
                traffic network. We use a stochastic online
                learning framework for the population dynamics,
                which is known to converge to the Nash equilibrium
                of the routing game. We analyze the sensitivity of
                this process and provide theoretical guarantees on
                the convergence rates as well as differential
                privacy values for these models. We confirm these
                with simulations on a small example.},
      URL = {http://cps-forces.org/pubs/75.html}
  }
  

Posted by Carolyn Winter on 10 Jun 2015.
Groups: forces
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