Differential Privacy in Cyber-Physical Systems
George Pappas

Citation
George Pappas. "Differential Privacy in Cyber-Physical Systems". Talk or presentation, 17, October, 2013; Science of CPS Keynote at the NSF 2013 CPS Principal Investigators' Meeting, Arlington. .

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
    George Pappas. <a
    href="http://www.terraswarm.org/pubs/148.html"><i>Differential
    Privacy in Cyber-Physical Systems</i></a>, Talk
    or presentation,  17, October, 2013; Science of CPS Keynote
    at the NSF <a
    href="http://cps-vo.org/group/pimtg13" >
    2013
    CPS Principal Investigators' Meeting</a>, Arlington.
    .
  • Plain text
    George Pappas. "Differential Privacy in Cyber-Physical
    Systems". Talk or presentation,  17, October, 2013;
    Science of CPS Keynote at the NSF <a
    href="http://cps-vo.org/group/pimtg13" >
    2013
    CPS Principal Investigators' Meeting</a>, Arlington.
    .
  • BibTeX
    @presentation{Pappas13_DifferentialPrivacyInCyberPhysicalSystems,
        author = {George Pappas},
        title = {Differential Privacy in Cyber-Physical Systems},
        day = {17},
        month = {October},
        year = {2013},
        note = {Science of CPS Keynote at the NSF <a
                  href="http://cps-vo.org/group/pimtg13" >
    2013 CPS
                  Principal Investigators' Meeting</a>, Arlington.
    },
        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/148.html}
    }
    

Posted by Russ Bielawski on 17 Oct 2013.
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