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What Can We Learn Privately?

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5 Author(s)
Kasiviswanathan, S.P. ; Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA ; Lee, H.K. ; Nissim, K. ; Raskhodnikova, S.
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Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals. We present several basic results that demonstrate general feasibility of private learning and relate several models previously studied separately in the contexts of privacy and standard learning.

Published in:

Foundations of Computer Science, 2008. FOCS '08. IEEE 49th Annual IEEE Symposium on

Date of Conference:

25-28 Oct. 2008