Computational Differential Privacy

  • Authors:

  • Ilya Mironov;Omkant Pandey;Omer Reingold;Salil Vadhan

  • Affiliations:

  • Microsoft Research;University of California, Los Angeles;Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel 76100;School of Engineering and Applied Sciences and Center for Research on Computation and Society, Harvard University,

  • Venue:
  • CRYPTO '09 Proceedings of the 29th Annual International Cryptology Conference on Advances in Cryptology
  • Year:
  • 2009

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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.