Sensitivity-independent differential privacy via prior knowledge refinement

International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems

Volumn 20, Issue 6, 2012, Pages 855-876

  Soria Comas, Jordi ^a   Domingo Ferrer, Josep ^a  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

Author keywords

Differential privacy; Knowledge refinement; Statistical databases

Indexed keywords

INFORMATION SYSTEMS; SOFTWARE ENGINEERING;

COMPLEX COMPUTATION; DATA QUALITY; DIFFERENTIAL PRIVACIES; KNOWLEDGE REFINEMENT; MECHANISM-BASED; NEW MECHANISMS; NOISE ADDITION; PRIOR-KNOWLEDGE; QUERY FUNCTIONS; STATISTICAL DATABASE;

ARTIFICIAL INTELLIGENCE;

EID: 84870935528     PISSN: 02184885     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218488512400272     Document Type: Article

References (13)

1. C. Dwork, F. McSherry, K. Nissim and A. Smith, Calibrating noise to sensitivity in private data analysis, in Proc. 3rd Theory of Cryptography Conf. (TCC 2006), LNCS 3876, Springer, 2006, pp. 265-284.
2. C. Dwork, Differential privacy, in Automata, Languages and Programming (ICALP 2006), LNCS 4052, Springer, 2006, pp. 1-12.
3. K. Nissim, S. Raskhodnikova and A. Smith, Smooth sensitivity and sampling in private data analysis, in Proc. 39th annual ACM Symp. Theory of Computing (STOC 2007), ACM, 2007, pp. 75-84.
4. F. McSherry, Mechanism design via differential privacy, in Proc. 48th Annual Symp. Foundations of Computer Science (FOCS 2007), IEEE Computer Society, 2007, pp. 94-103.
5. B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry and K. Talwar, Privacy, accuracy, and consistency too: a holistic solution to contingency table release, in Proc. 26th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS 2007), ACM, 2007, pp. 273-282.
6. X. Xiao, G. Wang and J. Gehrke, Differential privacy via wavelet transforms, IEEE Trans. Knowledge and Data Engineering 23(8) (2011) 1200-1214.
7. A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke and L. Vilhuber, Privacy: from theory to practice on the map, in Proc. ICDE 2008, IEEE, 2008, pp. 277-286.
8. J. Abowd and L. Vilhuber, How protective are synthetic data?, in Privacy in Statistical Databases (PSD 2008), LNCS 5262, Springer, 2008, pp. 239-246.
9. A. Charest, How can we analyze differentially-private synthetic datasets?, J. Privacy and Confidentiality 2(2) (2010).
10. A. Hundepool, J. Domingo-Ferrer, L. Franconi, S. Giessing, E. Schulte Nordholt, K. Spicer and P.-P. de Wolf, Statistical Disclosure Control (Wiley, 2012).
11. J. Soria-Comas and J. Domingo-Ferrer, Differential privacy through knowledge refinement, in 2012 ASE/IEEE Int. Conf. Privacy, Security, Risk and Trust (PASSAT 2012), Amsterdam, The Netherlands, September 3-6, 2012.
12. R. Sarathy and K. Muralidhar, Evaluating Laplace noise addition to satisfy differential privacy for numeric data, Trans. Data Privacy 4(1) (2011) 1-17.
13. C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov and M. Naor, Our data, ourselves: privacy via distributed noise generation, in Advances in Cryptology (EUROCRYPT 2006), LNCS 4004, Springer, 2006, pp. 486-503.