Optimal data-independent noise for differential privacy-Reference-Cited by-同舟云学术

Optimal data-independent noise for differential privacy

Published:2013-11 Issue: Volume:250 Page:200-214
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Soria-Comas Jordi,Domingo-Ferrer Josep

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference17 articles.

1. A. Blum, C. Dwork, F. McSherry, K. Nissim, Practical privacy: the SuLQ framework, in: Proceedings of the 24th ACM Symposium on Principles of Database Systems – PODS 2005, 2005, pp. 128–138.

2. A. Blum, K. Ligett, A. Roth, A learning theory approach to non-interactive database privacy, in: Proceedings of the 40th Annual ACM Symposium on Theory of Computing – STOC 2008, 2008, pp. 609–618.

3. I. Dinur, K. Nissim. Revealing information while preserving privacy, in: Proceedings of the 32nd ACM Symposium on Principles of Database Systems, 2003, pp. 202–210.

4. C. Dwork, K. Nissim, Privacy-preserving datamining on vertically partitioned databases, in; Proceedings of the 24th Annual International Cryptology Conference – CRYPTO 2004, 2004, pp. 528–544.

5. C. Dwork. Differential privacy, in: 33rd International Colloquium on Automata, Languages and Programming – ICALP 2006, Part II, volume 4052 of Lecture Notes in Computer Science, 2006, pp. 1–12.

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