Differentially private convex optimization with piecewise affine objectives
Shuo Han, Ufuk Topcu, George Pappas

Citation
Shuo Han, Ufuk Topcu, George Pappas. "Differentially private convex optimization with piecewise affine objectives". IEEE Conference on Decision and Control, 2014.

Abstract
Differential privacy is a recently proposed notion of privacy that provides strong privacy guarantees without any assumptions on the adversary. The paper studies the problem of computing a differentially private solution to convex optimization problems whose objective function is piecewise affine. Such problem is motivated by applications in which the affine functions that define the objective function contain sensitive user information. We propose several privacy preserving mechanisms and provide analysis on the trade-offs between optimality and the level of privacy for these mechanisms. Numerical experiments are also presented to evaluate their performance in practice.

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Citation formats

• HTML

  Shuo Han, Ufuk Topcu, George Pappas. <a
  href="http://www.terraswarm.org/pubs/285.html"
  >Differentially private convex optimization with
  piecewise affine objectives</a>, IEEE Conference on
  Decision and Control, 2014.

• Plain text

  Shuo Han, Ufuk Topcu, George Pappas. "Differentially
  private convex optimization with piecewise affine
  objectives". IEEE Conference on Decision and Control,
  2014.

• BibTeX

  @inproceedings{HanTopcuPappas14_DifferentiallyPrivateConvexOptimizationWithPiecewise,
      author = {Shuo Han and Ufuk Topcu and George Pappas},
      title = {Differentially private convex optimization with
                piecewise affine objectives},
      booktitle = {IEEE Conference on Decision and Control},
      year = {2014},
      abstract = {Differential privacy is a recently proposed notion
                of privacy that provides strong privacy guarantees
                without any assumptions on the adversary. The
                paper studies the problem of computing a
                differentially private solution to convex
                optimization problems whose objective function is
                piecewise affine. Such problem is motivated by
                applications in which the affine functions that
                define the objective function contain sensitive
                user information. We propose several privacy
                preserving mechanisms and provide analysis on the
                trade-offs between optimality and the level of
                privacy for these mechanisms. Numerical
                experiments are also presented to evaluate their
                performance in practice. },
      URL = {http://terraswarm.org/pubs/285.html}
  }
  

Posted by Shuo Han on 21 Mar 2014.