A framework for high-accuracy privacy-preserving mining

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2005, Pages 193-204

  Agrawal, Shipra ^a   Haritsa, Jayant R ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CLIENT PRIVACY; MATRIX ELEMENTS; RANDOM PERTURBATION;

DATA PRIVACY; DATABASE SYSTEMS; MATRIX ALGEBRA; PERTURBATION TECHNIQUES;

DATA MINING;

EID: 28444434728     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2005.8     Document Type: Conference Paper

References (18)

1. C. Aggarwal and P. Yu. A condensation approach to privacy preserving data mining. Proc. of 9th Intl. Conf. on Extending Database Technology (EDBT), March 2004.
2. D. Agrawal and C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. Proc. of ACM Symp. on Principles of Database Systems (PODS), May 2001.
3. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proc. of ACM SIGMOD Intl. Conf. on Management of Data, June 1993.
4. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. Proc. of 20th Intl. Conf. on Very Large Data Bases (VLDB), September 1994.
5. R. Agrawal and R. Srikant. Privacy-preserving data mining. Proc. of ACM SIGMOD Intl. Conf. on Management of Data, May 2000.
6. S. Agrawal and J. Haritsa. A framework for high-accuracy privacy-preserving mining. Technical Report TR-2004-02, Database Systems Lab, Indian Institute of Science, 2004. (http://dsl.serc.iisc.ernet.in/pub/TR/TR- 2004-02.pdf).
7. S. Agrawal and J. Haritsa. On addressing efficiency concerns in privacy-preserving mining. Proc. of 9th Intl. Conf. on Database Systems for Advanced Applications (DASFAA), March 2004.
8. P. de Wolf, J. Gouweleeuw, P. Kooiman, and L. Willenborg. Reflections on PRAM. Proc. of Statistical Data Protection Conf., March 1998.
9. A. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. Proc. of ACM Symp. on Principles of Database Systems (PODS), June 2003.
10. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. Proc. of 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD), July 2002.
11. W. Feller. An Introduction to Probability Theory and its Applications (Vol. I). Wiley, 1988.
12. J. Gouweleeuw, P. Kooiman, L. Willenborg, and P. de Wolf. Post randomisation for statistical disclosure control: Theory and implementation. Journal of Official Statistics, 14(4), 1998.
13. H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. Proc. of the 3rd IEEE Intl. Conf. on Data Mining (ICDM), Melbourne, Florida, December 2003.
14. S. Rizvi and J. Haritsa. Maintaining data privacy in association rule mining. Proc. of 28th Intl. Conf. on Very Large Databases (VLDB), August 2002.
15. G. Strang. Linear Algebra and its Applications. Thomson Learning Inc., 1988.
16. Y. Wang. On the number of successes in independent trials. Statistica Silica 3, 1993.
17. http://dataferrett.census.gov.
18. http://www.ics.uci.edu/mlearn/mlrepository.html.