Deriving private information from perturbed data using IQR based approach

ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops

Volumn , Issue , 2006, Pages

  Guo, Songtao ^a   Wu, Xintao ^a   Li, Yingjiu ^b  

^a UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE   (United States)

^b SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVE NOISE; DATA MINING; RANDOM PROCESSES; TECHNICAL PRESENTATIONS;

CONTINUOUS DATA; DATA VALUES; MODEL-BASED OPC; PRIVACY BREACHES; PRIVACY PRESERVING DATA MINING; PRIVATE INFORMATION; RANDOM PERTURBATIONS; SENSITIVE DATAS;

DATA PRIVACY;

EID: 84991000701     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDEW.2006.47     Document Type: Conference Paper

References (17)

1. D. Agrawal and C. Agrawal. 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 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.
3. F. Chin and G. Ozsoyoglu. Auditing and inference control in statistical databases. IEEE Transactions on Software Engineering, 8(6):574-582, 1982.
4. C. Clifton, M. Kantarcioglu, and J. Vaidya. Defining privacy for data mining, chapter NextGenerationData Mining. AAAI MIT Press, 2005.
5. W. Conover. Practical nonparametric statistics. Wiley, 1998.
6. J. Domingo-Ferrer. Current directions in statistical data protection. In Proceedings of the Statistical Data Protection, 1998.
7. J. Domingo-Ferrer, F. Sebé, and J. Castellà-Roca. On the security of noise addition for privacy in statistical databases. In Privacy in Statistical Databases, pages 149-161, 2004.
8. G. Duncan, S. Fienberg, R. Krishnan, R. Padman, and S. Roehrig. Disclosure limitation methods and information loss for tabular data. In Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, 2001.
9. S. Guo and X. Wu. On the use of spectral filtering for privacy preserving data mining. In Proc. of the 21st ACM Symposium on Applied Computing, April 2006.
10. 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.
11. R. Johnson and D.Wichern. Applied Multivariate Statistical Analysis. Prentice Hall, 1988.
12. M. Kantarcioglu, J.Jin, and C. Clifton. When do data mining results violate privacy? In Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2004.
13. H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In Proc. of the 3rd Int'l Conf. on Data Mining, pages 99-106, 2003.
14. K. Muralidhar and R. Sarathy. A general additive data perturbation method for datbase security. Management Science, 45(10):1399-1415, 2002.
15. R. Sarathy and K. Muralidhar. The security of confidential numerical data in databases. Information Systems Research, 13(4):389-404, 2002.
16. L. Willenborg and T. D. Wall. Elements of statistical disclosure control. Lecture Notes in Statistics, Springer Verlag, 155, 2000.
17. Y. Zhu and L. Liu. Optimal randomization for privacy preserving data mining. In Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2004.