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Annual International Cryptology Conference

CRYPTO 2009: Advances in Cryptology - CRYPTO 2009 pp 126–142Cite as

Computational Differential Privacy

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Part of the Lecture Notes in Computer Science book series (LNSC,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.

Keywords

  • Secret Sharing Scheme
  • Full Version
  • Differential Privacy
  • Coin Toss
  • Probabilistic Polynomial Time

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Microsoft Research, Silicon Valley Campus

    Ilya Mironov

  2. University of California, Los Angeles

    Omkant Pandey

  3. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, 76100, Israel

    Omer Reingold

  4. School of Engineering and Applied Sciences and Center for Research on Computation and Society, Harvard University, USA

    Salil Vadhan

Authors
  1. Ilya Mironov
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  2. Omkant Pandey
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  3. Omer Reingold
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  4. Salil Vadhan
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Editor information

Editors and Affiliations

  1. IBM Research, Hawthorne, NY, USA

    Shai Halevi

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Mironov, I., Pandey, O., Reingold, O., Vadhan, S. (2009). Computational Differential Privacy. In: Halevi, S. (eds) Advances in Cryptology - CRYPTO 2009. CRYPTO 2009. Lecture Notes in Computer Science, vol 5677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03356-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-03356-8_8

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