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