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Computational Differential Privacy

  • Conference paper

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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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03355-1

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