Computational Differential Privacy

  • Ilya Mironov
  • Omkant Pandey
  • Omer Reingold
  • Salil Vadhan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5677)

Abstract

The definition of differential privacy has recently emerged as a leading standard of privacy guarantees for algorithms on statistical databases. We offer several relaxations of the definition which require privacy guarantees to hold only against efficient—i.e., computationally-bounded—adversaries. We establish various relationships among these notions, and in doing so, we observe their close connection with the theory of pseudodense sets by Reingold et al.[1]. We extend the dense model theorem of Reingold et al. to demonstrate equivalence between two definitions (indistinguishability- and simulatability-based) of computational differential privacy.

Our computational analogues of differential privacy seem to allow for more accurate constructions than the standard information-theoretic analogues. In particular, in the context of private approximation of the distance between two vectors, we present a differentially-private protocol for computing the approximation, and contrast it with a substantially more accurate protocol that is only computationally differentially private.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ilya Mironov
    • 1
  • Omkant Pandey
    • 2
  • Omer Reingold
    • 3
  • Salil Vadhan
    • 4
  1. 1.Microsoft Research
  2. 2.University of CaliforniaLos Angeles
  3. 3.Department of Computer Science and Applied MathematicsWeizmann Institute of ScienceRehovotIsrael
  4. 4.School of Engineering and Applied Sciences and Center for Research on Computation and SocietyHarvard UniversityUSA

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