On the complexity of differentially private data release :Efficient algorithms and hardness results

Proceedings of the Annual ACM Symposium on Theory of Computing

Volumn , Issue , 2009, Pages 381-390

  Dwork, Cynthia ^a   Naor, Moni ^b   Reingold, Omer ^b   Rothblum, Guy N ^c   Vadhan, Salil ^d  

^a MICROSOFT RESEARCH   (United States)

^b WEIZMANN INSTITUTE OF SCIENCE   (Israel)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d HARVARD UNIVERSITY   (United States)

Author keywords

Algorithms; Security; Theory

Indexed keywords

COMPUTATIONAL FEASIBILITY; COMPUTATIONAL PROCEDURES; DATA ANALYSTS; DATA ITEMS; DATA SETS; EFFICIENT ALGORITHM; HARDNESS RESULT; LARGE DATA; NON-INTERACTIVE; PRIVATE DATA; PRIVATE INFORMATION; SANITIZATION; SECURITY; SYNTHETIC DATASETS; THEORY; TRAITOR TRACING SCHEMES; UTILITY MEASURE;

ALGORITHMS; COMPUTATION THEORY; COMPUTATIONAL EFFICIENCY; DATA STRUCTURES; HARDNESS;

SET THEORY;

EID: 70350689921     PISSN: 07378017     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1536414.1536467     Document Type: Conference Paper

References (20)

1. A. Blum, K. Ligett, and A. Roth. A learning theory approach to non-interactive database privacy. In STOC, pages 609-618, 2008.
2. D. Boneh and M. Naor. Traitor tracing with constant size ciphertext. In ACM Conference on Computer and Communications Security, pages 501-510, 2008.
3. D. Boneh, A. Sahai, and B. Waters. Fully collusion resistant traitor tracing with short ciphertexts and private keys. In EUROCRYPT, pages 573-592, 2006.
4. B. Chor, A. Fiat, and M. Naor. Tracing traitors. In CRYPTO, pages 257-270, 1994.
5. I. Dinur and K. Nissim. Revealing information while preserving privacy. In PODS, pages 202-210, 2003.
6. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In S. Halevy and T. Rabin, editors, First Theory of Cryptography Conference (TCC), volume 3876, pages 265-284. Springer-Verlag, 2006.
7. C. Dwork, F. McSherry, and K. Talwar. The price of privacy and the limits of lp decoding. In STOC, pages 85-94, 2007.
8. C. Dwork and K. Nissim. Privacy-preserving datamining on vertically partitioned databases. In CRYPTO, pages 528-544, 2004.
9. C. Dwork and S. Yekhanin. New efficient attacks on statistical disclosure control mechanisms. In CRYPTO, pages 469-480, 2008.
10. D. Feldman, A. Fiat, H. Kaplan, and K. Nissim. Private coresets. These Proceedings, 2009.
11. O. Goldreich. The Foundations of Cryptography - Volume 2. Cambridge University Press, 2004.
12. O. Goldreich, S. Goldwasser, and S. Micali. How to construct pseudorandom functions. Journal of the ACM, 33(2):792-807, 1986.
13. S. Goldwasser and S. Micali. Probabilistic encryption. Journal of Computer and System Sciences, 28(2):270-299, 1984.
14. R. Impagliazzo, R. Jaiswal, V. Kabanets, and A. Wigderson. Uniform direct product theorems: simplified, optimized, and derandomized. In STOC, pages 579{588, 2008.
15. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith. What can we learn privately? In FOCS, pages 1-19, 2008.
16. A. Kiayias and M. Yung. Self protecting pirates and black-box traitor tracing. In CRYPTO, pages 63-79, 2001.
17. F. McSherry and K. Talwar. Mechanism design via differential privacy. In FOCS, pages 94-103. IEEE Computer Society, 2007.
18. 3/2). J. Comput. Syst. Sci., 58(2):376-403, 1999.
19. R. E. Schapire. Theoretical views of boosting and applications. In ATL, pages 13-25, 1999.
20. L. G. Valiant. A theory of the learnable. Commun. ACM, 27(11):1134-1142, 1984.