Differential Privacy: A Survey of Results

  • Cynthia Dwork
Conference paper

DOI: 10.1007/978-3-540-79228-4_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4978)
Cite this paper as:
Dwork C. (2008) Differential Privacy: A Survey of Results. In: Agrawal M., Du D., Duan Z., Li A. (eds) Theory and Applications of Models of Computation. TAMC 2008. Lecture Notes in Computer Science, vol 4978. Springer, Berlin, Heidelberg

Abstract

Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database.

In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cynthia Dwork
    • 1
  1. 1.Microsoft Research 

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