Differentially Private Filtering
Jerome Le Ny, George Pappas

Citation
Jerome Le Ny, George Pappas. "Differentially Private Filtering". IEEE Transactions on Automatic Control, 59(2):341-354, February 2013; To appear in February 2014 (prepress).

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. This 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.

Electronic downloads

Internal. This publication has been marked by the author for TerraSwarm-only distribution, so electronic downloads are not available without logging in.

Citation formats

• HTML

  Jerome Le Ny, George Pappas. <a
  href="http://www.terraswarm.org/pubs/83.html"
  >Differentially Private Filtering</a>,
  <i>IEEE Transactions on Automatic Control</i>,
  59(2):341-354, February 2013; To appear in February 2014
  (prepress).

• Plain text

  Jerome Le Ny, George Pappas. "Differentially Private
  Filtering". <i>IEEE Transactions on Automatic
  Control</i>, 59(2):341-354, February 2013; To appear
  in February 2014 (prepress).

• BibTeX

  @article{NyPappas13_DifferentiallyPrivateFiltering,
      author = {Jerome Le Ny and George Pappas},
      title = {Differentially Private Filtering},
      journal = {IEEE Transactions on Automatic Control},
      volume = {59},
      number = {2},
      pages = {341 - 354},
      month = {February},
      year = {2013},
      note = {To appear in February 2014 (prepress).},
      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. This 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/83.html}
  }
  

Posted by Mila MacBain on 25 Jul 2013.