Differentially Private Distributed Protocol for Electric Vehicle Charging
Shuo Han, Ufuk Topcu, George Pappas

Citation
Shuo Han, Ufuk Topcu, George Pappas. "Differentially Private Distributed Protocol for Electric Vehicle Charging". 52nd Annual Allerton Conference on Communication, Control, and Computing, 1, October, 2014.

Abstract
In distributed electric vehicle (EV) charging, an optimization problem is solved iteratively between a central server and the charging stations by exchanging coordination signals that are publicly available to all stations. The coordi- nation signals depend on user demand reported by charging stations and may reveal private information of the users at the stations. From the public signals, an adversary can potentially decode private user information and put user privacy at risk. This paper develops a distributed EV charging algorithm that preserves differential privacy, which is a notion of privacy recently introduced and studied in theoretical computer science. The algorithm is based on the so-called Laplace mechanism, which perturbs the public signal with Laplace noise whose mag- nitude is determined by the sensitivity of the public signal with respect to changes in user information. The paper derives the sensitivity and analyzes the suboptimality of the differentially private charging algorithm. In particular, we obtain a bound on suboptimality by viewing the algorithm as an implementation of stochastic gradient descent. In the end, numerical experiments are performed to investigate various aspects of the algorithm when being used in practice, including the choice of step size, number of iterations, and tradeoffs between privacy level and suboptimality.

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Citation formats

• HTML

  Shuo Han, Ufuk Topcu, George Pappas. <a
  href="http://www.terraswarm.org/pubs/336.html"
  >Differentially Private Distributed Protocol for Electric
  Vehicle Charging</a>, 52nd Annual Allerton Conference
  on Communication, Control, and Computing, 1, October, 2014.

• Plain text

  Shuo Han, Ufuk Topcu, George Pappas. "Differentially
  Private Distributed Protocol for Electric Vehicle
  Charging". 52nd Annual Allerton Conference on
  Communication, Control, and Computing, 1, October, 2014.

• BibTeX

  @inproceedings{HanTopcuPappas14_DifferentiallyPrivateDistributedProtocolForElectricVehicle,
      author = {Shuo Han and Ufuk Topcu and George Pappas},
      title = {Differentially Private Distributed Protocol for
                Electric Vehicle Charging},
      booktitle = {52nd Annual Allerton Conference on Communication,
                Control, and Computing},
      day = {1},
      month = {October},
      year = {2014},
      abstract = {In distributed electric vehicle (EV) charging, an
                optimization problem is solved iteratively between
                a central server and the charging stations by
                exchanging coordination signals that are publicly
                available to all stations. The coordi- nation
                signals depend on user demand reported by charging
                stations and may reveal private information of the
                users at the stations. From the public signals, an
                adversary can potentially decode private user
                information and put user privacy at risk. This
                paper develops a distributed EV charging algorithm
                that preserves differential privacy, which is a
                notion of privacy recently introduced and studied
                in theoretical computer science. The algorithm is
                based on the so-called Laplace mechanism, which
                perturbs the public signal with Laplace noise
                whose mag- nitude is determined by the sensitivity
                of the public signal with respect to changes in
                user information. The paper derives the
                sensitivity and analyzes the suboptimality of the
                differentially private charging algorithm. In
                particular, we obtain a bound on suboptimality by
                viewing the algorithm as an implementation of
                stochastic gradient descent. In the end, numerical
                experiments are performed to investigate various
                aspects of the algorithm when being used in
                practice, including the choice of step size,
                number of iterations, and tradeoffs between
                privacy level and suboptimality.},
      URL = {http://terraswarm.org/pubs/336.html}
  }
  

Posted by Shuo Han on 7 Jul 2014.
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