Privacy and Truthfulness in Electric Vehicle Charging Protocols
Shuo Han, Ufuk Topcu, George Pappas

Citation
Shuo Han, Ufuk Topcu, George Pappas. "Privacy and Truthfulness in Electric Vehicle Charging Protocols". Talk or presentation, September, 2014; Poster presented at the TerraSwarm 2014 Security and Privacy Workshop, Rochester, NY.

Abstract
In electric vehicle (EV) charging, the goal is to compute a charging schedule that meets all user specifications while minimizing the influence on the power grid. Usually, an optimization problem is solved iteratively between a central server (mediator) and the charging stations by exchanging coordination signals that are publicly available to all stations. In this work, we address two potential issues in this procedure: (1) privacy of the participating users and (2) manipulation of the charging schedule by malicious users. During communication, the coordination signals depend on user demands 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. We develop 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 magnitude is determined by the sensitivity of the public signal with respect to changes in user information. The suboptimality of the charging algorithm is shown to decrease as O(1/n), where n is the number of participating users. Another useful consequence of differential privacy is truthfulness of the participating users. Differential privacy can limit the power of each user in manipulating the scheduling process by being insensitive to changes in user specifications. As a result, a user does not benefit much from misreporting its specifications, which leads to truth-telling behaviors. In particular, the incentives of misreporting decrease as the level of privacy increases.

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Citation formats  
  • HTML
    Shuo Han, Ufuk Topcu, George Pappas. <a
    href="http://www.terraswarm.org/pubs/367.html"><i>Privacy
    and Truthfulness in Electric Vehicle Charging
    Protocols</i></a>, Talk or presentation, 
    September, 2014; Poster presented at the <a
    href="http://www.terraswarm.org/conferences/14/security/index.htm"
    >TerraSwarm 2014 Security and Privacy Workshop</a>,
    Rochester, NY.
  • Plain text
    Shuo Han, Ufuk Topcu, George Pappas. "Privacy and
    Truthfulness in Electric Vehicle Charging Protocols".
    Talk or presentation,  September, 2014; Poster presented at
    the <a
    href="http://www.terraswarm.org/conferences/14/security/index.htm"
    >TerraSwarm 2014 Security and Privacy Workshop</a>,
    Rochester, NY.
  • BibTeX
    @presentation{HanTopcuPappas14_PrivacyTruthfulnessInElectricVehicleChargingProtocols,
        author = {Shuo Han and Ufuk Topcu and George Pappas},
        title = {Privacy and Truthfulness in Electric Vehicle
                  Charging Protocols},
        month = {September},
        year = {2014},
        note = {Poster presented at the <a
                  href="http://www.terraswarm.org/conferences/14/security/index.htm"
                  >TerraSwarm 2014 Security and Privacy
                  Workshop</a>, Rochester, NY.},
        abstract = {In electric vehicle (EV) charging, the goal is to
                  compute a charging schedule that meets all user
                  specifications while minimizing the influence on
                  the power grid. Usually, an optimization problem
                  is solved iteratively between a central server
                  (mediator) and the charging stations by exchanging
                  coordination signals that are publicly available
                  to all stations. In this work, we address two
                  potential issues in this procedure: (1) privacy of
                  the participating users and (2) manipulation of
                  the charging schedule by malicious users. During
                  communication, the coordination signals depend on
                  user demands 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. We develop 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 magnitude
                  is determined by the sensitivity of the public
                  signal with respect to changes in user
                  information. The suboptimality of the charging
                  algorithm is shown to decrease as O(1/n), where n
                  is the number of participating users. Another
                  useful consequence of differential privacy is
                  truthfulness of the participating users.
                  Differential privacy can limit the power of each
                  user in manipulating the scheduling process by
                  being insensitive to changes in user
                  specifications. As a result, a user does not
                  benefit much from misreporting its specifications,
                  which leads to truth-telling behaviors. In
                  particular, the incentives of misreporting
                  decrease as the level of privacy increases.},
        URL = {http://terraswarm.org/pubs/367.html}
    }
    

Posted by Shuo Han on 17 Sep 2014.
Groups: services tools

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