Privacy preserving categorical data analysis with unknown distortion parameters

Transactions on Data Privacy

Volumn 2, Issue 3, 2009, Pages 185-205

  Guo, Ling ^a   Wu, Xintao ^a  

^a University of North Carolina at Charlotte   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION MEASURES; CATEGORICAL DATA; DATA MINERS; DATA MINING TASKS; DISTORTION PARAMETERS; PRIVACY PRESERVING; RANDOMIZED RESPONSE; STATISTICAL ANALYSIS;

DATA HANDLING;

DATA PRIVACY;

EID: 78049261783     PISSN: 18885063     EISSN: 20131631     Source Type: Journal    
DOI: None     Document Type: Article

References (37)

1. D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the 20th Symposium on Principles of Database Systems, 2001.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD Conference, pages 207-216, 1993.
3. R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 439-450. Dallas, Texas, May 2000.
4. S. Agrawal and J. R. Haritsa. A framework for high-accuracy privacy-preserving mining. In Proceedings of the 21st IEEE International Conference on Data Engineering, pages 193-204, 2005.
5. A. Agresti. Categorical data analysis. Wiley, 2002.
6. R. Brand. Microdata protection through noise addition. Lecture Notes in Computer Science, 2316:97-116, 2002.
7. A. Chaudhuri and R. Mukerjee. Randomized response: theory and techniques. Marcel Dekker, 1988.
8. T. T. Chen. Analysis of randomized response as purposively misclassified data. Journal of the American Statistical Association, pages 158-163, 1979.
9. J. Domingo-Ferrer, J.M. Mateo-Sanz, and V. Torra. Comparing SDC methods for micro-data on the basis of information loss and disclosure risk. In Proceedings of NTTS and ETK, 2001.
10. W. Du, Z. Teng, and Z. Zhu. Privacy-maxent: integrating background knowledge in privacy quantification. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 459-472, 2008.
11. W. Du and Z. Zhan. Using randomized response techniques for privacy-preserving data mining. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 505-510, 2003.
12. W. DuMouchel and D. Pregibon. Empirical bayes screening for multi-item association. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. San Francisco, CA, August 2001.
13. A. Evfimievski. Randomization in privacy preserving data mining. ACM SIGKDD Explorations Newsletter, 4(2):43-48, 2002.
14. A. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In Proceedings of the 22nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 211-222, 2003.
15. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 217-228, 2002.
16. L. Geng and H. J. Hamilton. Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3):9, 2006.
17. S. Gomatam and A. F. Karr. Distortion measures for categorical data swapping. Technical Report, Number 131, National Institute of Statistical Sciences, 2003.
18. J. M. Gouweleeuw, P. Kooiman, L. C. R. J. Willenborg, and P. P. de Wolf. Post randomization for statistical disclosure control: theory and implementation. Journal of Official Statistics, 14(4):463-478, 1998.
19. L. Guo, S. Guo, and X. Wu. Privacy preserving market basket data analysis. In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2007.
20. L. Guo, S. Guo, and X. Wu. On addressing accuracy concerns in privacy and preserving association rule mining. In Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2008.
21. M. Hay, G. Miklau, D. Jensen, P. Weis, and S. Srivastava. Anonymizing social networks. Technical Report, University of Massachusetts, 07-19, 2007.
22. Z. Huang and W. Du. Optrr: Optimizing randomized response schemes for privacy-preserving data mining. In Proceedings of the 24th IEEE International Conference on Data Engineering, pages 705-714, 2008.
23. Z. Huang, W. Du, and B. Chen. Deriving private information from randomized data. In Proceedings of the ACM SIGMOD Conference on Management of Data. Baltimore, MA, 2005.
24. H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In Proceedings of the 3rd International Conference on Data Mining, pages 99-106, 2003.
25. J. Kim. A method for limiting disclosure in microdata based on random noise and transformation. In Proceedings of the American Statistical Association on Survey Research Methods, 1986.
26. E. L. Korn. Hierarchical log-linear models not preserved by classification error. Journal of the American Statistical Association, 76:110-113, 1981.
27. K. Liu and E. Terzi. Towards identity anonymization on graphs. In Proceedings of the ACM SIGMOD Conference, Vancouver, Canada, 2008. ACM Press.
28. D. J. Martin, D. Kifer, A. Machanavajjhala, J. Gehrke, and J. Y. Halpern. Worst-case background knowledge in privacy. Technical Report, Cornell University, 2006.
29. G. Piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases, pages 229-248, 1991.
30. J. R. Quinlan. C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
31. S. J. Rizvi and J. R. Haritsa. Maintaining data privacy in association rule mining. In Proceedings of the 28th International Conference on Very Large Data Bases, 2002.
32. C. Silverstein, S. Brin, and R. Motwani. Beyond market baskets: generalizing association rules to dependence rules. Data Mining and Knowledge Discovery, 2:39-68, 1998.
33. P. Tan, V. Kumar, and J. Srivastava. Selecting the right interestingness measure for association patterns. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining, pages 32-41, 2002.
34. P. Tan, M. Steinbach, and K. Kumar. Introduction to data mining. Addison Wesley, 2006.
35. A. Van den Hot. Analyzing misclassified data: randomized response and post randomization. Ph.D. Thesis, University of Utrecht, 2004.
36. L. Willenborg and T. De Waal. Elements of statistical disclosure control in practice. Lecture Notes in Statistics, 155, 2001.
37. X. Ying and X. Wu. Randomizing social networks: a spectrum preserving approach. In Proceedings of the 8th SIAM Conference on Data Mining, April 2008.