PrivBayes: Private data release via Bayesian networks

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2014, Pages 1423-1434

  Zhang, Jun ^a   Cormode, Graham ^b   Procopiuc, Cecilia M ^c   Srivastava, Divesh ^c   Xiao, Xiaokui ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF WARWICK   (United Kingdom)

^c AT AND T LABS RESEARCH   (United States)

Author keywords

Bayesian network; Differential privacy; Synthetic data generation

Indexed keywords

DATA PRIVACY;

CURSE OF DIMENSIONALITY; DIFFERENTIAL PRIVACIES; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL DATASET; MUTUAL INFORMATIONS; PRIVACY PROTECTION; SURROGATE FUNCTION; SYNTHETIC DATA GENERATIONS;

BAYESIAN NETWORKS;

EID: 84904281795     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (40)

1. Integrated public use microdata series international. https://international.ipums.org.
2. Statlib. http://lib.stat.cmu.edu/.
3. Transaction processing performance council. http://www.tpc.org.
4. K. Bache and M. Lichman. UCI machine learning repository, 2013.
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 PODS, pages 273-282, 2007.
6. R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta. Discovering frequent patterns in sensitive data. In KDD, pages 503-512, 2010.
7. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. page 27, 2011.
8. K. Chaudhuri and C. Monteleoni. Privacy-preserving logistic regression. In NIPS, pages 289-296, 2008.
9. K. Chaudhuri, C. Monteleoni, and A. D. Sarwate. Differentially private empirical risk minimization. Journal of Machine Learning Research, 12:1069-1109, 2011.
10. D. M. Chickering, D. Heckerman, and C. Meek. Large-sample learning of bayesian networks is NP-Hard. Journal of Machine Learning Research, 5:1287-1330, 2004.
11. C. K. Chow and C. N. Liu. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14:462-467, 1968.
12. G. Cormode, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. Differentially private spatial decompositions. In ICDE, 2012.
13. G. Cormode, C. M. Procopiuc, D. Srivastava, and T. T. L. Tran. Differentially private publication of sparse data. In ICDT, 2012.
14. A. B. Cybakov. Introduction to nonparametric estimation. Springer, 2009.
15. B. Ding, M. Winslett, J. Han, and Z. Li. Differentially private data cubes: optimizing noise sources and consistency. In SIGMOD, pages 217-228, 2011.
16. C. Dwork. Differential privacy. In ICALP, pages 1-12, 2006.
17. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, pages 265-284, 2006.
18. D. Feldman, A. Fiat, H. Kaplan, and K. Nissim. Private coresets. In STOC, pages 361-370, 2009.
19. A. Friedman and A. Schuster. Data mining with differential privacy. In KDD, pages 493-502, 2010.
20. M. Hardt, K. Ligett, and F. McSherry. A simple and practical algorithm for differentially private data release. In NIPS, pages 2348-2356, 2012.
21. M. Hay, V. Rastogi, G. Miklau, and D. Suciu. Boosting the accuracy of differentially private histograms through consistency. PVLDB, 3(1):1021-1032, 2010.
22. D. Kifer, A. D. Smith, and A. Thakurta. Private convex optimization for empirical risk minimization with applications to high-dimensional regression. Journal of Machine Learning Research-Proceedings Track, 23:25.1-25.40, 2012.
23. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
24. C. Li, M. Hay, V. Rastogi, G. Miklau, and A. McGregor. Optimizing linear counting queries under differential privacy. In PODS, pages 123-134, 2010.
25. C. Li and G. Miklau. An adaptive mechanism for accurate query answering under differential privacy. PVLDB, 5(6):514-525, 2012.
26. C. Li and G. Miklau. Optimal error of query sets under the differentially-private matrix mechanism. In ICDT, pages 272-283, 2013.
27. N. Li, W. Qardaji, D. Su, and J. Cao. Privbasis: Frequent itemset mining with differential privacy. PVLDB, 5(11):1340-1351, 2012.
28. D. Margaritis. Learning Bayesian Network Model Structure from Data. PhD thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, May 2003.
29. F. McSherry and R. Mahajan. Differentially-private network trace analysis. In SIGCOMM, pages 123-134, 2010.
30. F. McSherry and I. Mironov. Differentially private recommender systems: Building privacy into the netflix prize contenders. In KDD, pages 627-636, 2009.
31. F. McSherry and K. Talwar. Mechanism design via differential privacy. In FOCS, pages 94-103, 2007.
32. P. Mohan, A. Thakurta, E. Shi, D. Song, and D. Culler. GUPT: privacy preserving data analysis made easy. In SIGMOD, pages 349-360, 2012.
33. K. Nissim, S. Raskhodnikova, and A. Smith. Smooth sensitivity and sampling in private data analysis. In STOC, pages 75-84, 2007.
34. V. Rastogi and S. Nath. Differentially private aggregation of distributed time-series with transformation and encryption. In SIGMOD, pages 735-746, 2010.
35. B. I. P. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft. Learning in a large function space: Privacy-preserving mechanisms for SVM learning. Journal of Privacy and Confidentiality, 4(1):65-100, 2012.
36. A. Smith. Privacy-preserving statistical estimation with optimal convergence rate. In STOC, 2011.
37. X. Xiao, G. Wang, and J. Gehrke. Differential privacy via wavelet transforms. In ICDE, pages 225-236, 2010.
38. G. Yaroslavtsev, G. Cormode, C. M. Procopiuc, and D. Srivastava. Accurate and efficient private release of datacubes and contingency tables. In ICDE, pages 745-756, 2013.
39. G. Yuan, Z. Zhang, M. Winslett, X. Xiao, Y. Yang, and Z. Hao. Low-rank mechanism: Optimizing batch queries under differential privacy. PVLDB, 5(11):1352-1363, 2012.
40. J. Zhang, X. Xiao, Y. Yang, Z. Zhang, and M. Winslett. PrivGene: differentially private model fitting using genetic algorithms. In SIGMOD, pages 665-676, 2013.