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Published in:
2008 | OriginalPaper | Chapter
Differential Privacy: A Survey of Results
Author : Cynthia Dwork
Published in: Theory and Applications of Models of Computation
Publisher: Springer Berlin Heidelberg
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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.