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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
verfasst von : Amos Beimel, Kobbi Nissim, Uri Stemmer
Erschienen in: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Verlag: Springer Berlin Heidelberg
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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.