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Private Learning and Sanitization: Pure vs. Approximate Differential Privacy

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Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX 2013, RANDOM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8096))

Abstract

We compare the sample complexity of private learning and sanitization tasks under pure ε-differential privacy [Dwork, McSherry, Nissim, and Smith TCC 2006] and approximate (ε,δ)-differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy.

Research supported by the Israel Science Foundation (grants No. 938/09 and 2761/12) and by the Frankel Center for Computer Science at Ben-Gurion University. Work done while the second author was a Visiting Scholar at the Harvard Center for Research on Computation and Society (CRCS). Work partially done when the third author was visiting Harvard University supported in part by NSF grant CNS-1237235 and a gift from Google, Inc.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Ben-Gurion University, Israel

    Amos Beimel, Kobbi Nissim & Uri Stemmer

  2. Dept. of Computer Science, Harvard University, Israel

    Kobbi Nissim

Authors
  1. Amos Beimel
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  2. Kobbi Nissim
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  3. Uri Stemmer
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Editor information

Editors and Affiliations

  1. University of California, Berkeley, CA, USA

    Prasad Raghavendra

  2. Dept. of computer Science and Engineering, Pennsylvania State University, University Park, PA, USA

    Sofya Raskhodnikova

  3. Institute of Computer Science, University of Kiel, 24118, Kiel, Germany

    Klaus Jansen

  4. University of Geneva, Centre Universitaire d’Informatique, 1227, Carouge, Switzerland

    José D. P. Rolim

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Beimel, A., Nissim, K., Stemmer, U. (2013). Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. In: Raghavendra, P., Raskhodnikova, S., Jansen, K., Rolim, J.D.P. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2013 2013. Lecture Notes in Computer Science, vol 8096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40328-6_26

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  • DOI: https://doi.org/10.1007/978-3-642-40328-6_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40327-9

  • Online ISBN: 978-3-642-40328-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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