Differential Privacy in Cloud-Based Control and Estimation
Fragkiskos Koufogiannis, Jerome Le Ny, George Pappas

Citation
Fragkiskos Koufogiannis, Jerome Le Ny, George Pappas. "Differential Privacy in Cloud-Based Control and Estimation". Talk or presentation, 5, November, 2013; Poster presented at the 2013 TerraSwarm Annual Meeting.

Abstract
Emerging systems such as smart grids or intelligent transportation systems often require end-user applications to continuously send information to external data aggregators performing monitoring or control tasks. This can result in an undesirable loss of privacy for the users in exchange of the benefits provided by the application. Motivated by this trend, this paper introduces privacy concerns in a system theoretic context, and addresses the problem of releasing filtered signals that respect the privacy of the user data streams. Our approach relies on a formal notion of privacy from the database literature, called differential privacy, which provides strong privacy guarantees against adversaries with arbitrary side information. Methods are developed to approximate a given filter by a differentially private version, so that the distortion introduced by the privacy mechanism is minimized. Two specific scenarios are considered. First, the notion of differential privacy is extended to dynamic systems with many participants contributing independent input signals. Kalman filtering is also discussed in this context, when a released output signal must preserve differential privacy for the measured signals or state trajectories of the individual participants. Second, differentially private mechanisms are described to approximate stable filters when participants contribute to a single event stream, extending previous work on differential privacy under continual observation.

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Citation formats

• HTML

  Fragkiskos Koufogiannis, Jerome Le Ny, George Pappas. <a
  href="http://www.terraswarm.org/pubs/164.html"><i>Differential
  Privacy in Cloud-Based Control and
  Estimation</i></a>, Talk or presentation,  5,
  November, 2013; Poster presented at the <a
  href="http://www.terraswarm.org/conferences/13/annual"
  >2013 TerraSwarm Annual Meeting</a>.

• Plain text

  Fragkiskos Koufogiannis, Jerome Le Ny, George Pappas.
  "Differential Privacy in Cloud-Based Control and
  Estimation". Talk or presentation,  5, November, 2013;
  Poster presented at the <a
  href="http://www.terraswarm.org/conferences/13/annual"
  >2013 TerraSwarm Annual Meeting</a>.

• BibTeX

  @presentation{KoufogiannisLeNyPappas13_DifferentialPrivacyInCloudBasedControlEstimation,
      author = {Fragkiskos Koufogiannis and Jerome Le Ny and
                George Pappas},
      title = {Differential Privacy in Cloud-Based Control and
                Estimation},
      day = {5},
      month = {November},
      year = {2013},
      note = {Poster presented at the <a
                href="http://www.terraswarm.org/conferences/13/annual"
                >2013 TerraSwarm Annual Meeting</a>.},
      abstract = {Emerging systems such as smart grids or
                intelligent transportation systems often require
                end-user applications to continuously send
                information to external data aggregators
                performing monitoring or control tasks. This can
                result in an undesirable loss of privacy for the
                users in exchange of the benefits provided by the
                application. Motivated by this trend, this paper
                introduces privacy concerns in a system theoretic
                context, and addresses the problem of releasing
                filtered signals that respect the privacy of the
                user data streams. Our approach relies on a formal
                notion of privacy from the database literature,
                called differential privacy, which provides strong
                privacy guarantees against adversaries with
                arbitrary side information. Methods are developed
                to approximate a given filter by a differentially
                private version, so that the distortion introduced
                by the privacy mechanism is minimized. Two
                specific scenarios are considered. First, the
                notion of differential privacy is extended to
                dynamic systems with many participants
                contributing independent input signals. Kalman
                filtering is also discussed in this context, when
                a released output signal must preserve
                differential privacy for the measured signals or
                state trajectories of the individual participants.
                Second, differentially private mechanisms are
                described to approximate stable filters when
                participants contribute to a single event stream,
                extending previous work on differential privacy
                under continual observation.},
      URL = {http://terraswarm.org/pubs/164.html}
  }
  

Posted by Fragkiskos Koufogiannis on 3 Nov 2013.