Bounds on the sample complexity for private learning and private data release-Reference-Cited by-同舟云学术

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Bounds on the sample complexity for private learning and private data release

Published:2013-09-18 Issue:3 Volume:94 Page:401-437
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Beimel Amos,Brenner Hai,Kasiviswanathan Shiva Prasad,Nissim Kobbi

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s10994-013-5404-1.pdf

Reference27 articles.

1. LNCS;A. Beimel,2010

2. Beimel, A., Nissim, K., & Stemmer, U. (2013). Characterizing the sample complexity of private learners. In ITCS (pp. 97–110).

3. Blum, A., Dwork, C., McSherry, F., & Nissim, K. (2005). Practical privacy: the SuLQ framework. In PODS (pp. 128–138). New York: ACM.

4. Blum, A., Ligett, K., & Roth, A. (2008). A learning theory approach to non-interactive database privacy. In STOC (pp. 609–618). New York: ACM.

5. Blum, A., Ligett, K., & Roth, A. (2013). A learning theory approach to non-interactive database privacy. Journal of the ACM, 60(2), 12.

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