Privacy-Preserving Data Mining: Balancing Insight and Security

1. Privacy Preserving Data Mining Benjamin Fung bfung(at)cs.sfu.ca

2. Privacy Preserving Data Mining • What is data mining? • Non-trivial extraction of implicit, previously unknown, and potentially useful information from large data sets or databases [W. Frawley and G. Piatetsky-Shapiro and C. Matheus, 1992] • What is privacy preserving data mining? • Study of achieving some data mining goals without scarifying the privacy of the individuals

3. Scenario (Information Sharing) • A data owner wants to release a person-specific data table to another party (or the public) for the purpose of classification analysis without scarifying the privacy of the individuals in the released data. Person-specific data Data owner Data recipients

4. Privacy Threat • If a description on (Education, Sex) is so specific that not many people match it, releasing the table will lead to linking a unique or a small number of individuals with sensitive information. Data recipients Adversary

5. Solution: Generalization

6. References • K. Wang, B. C. M. Fung, and P. S. Yu. Template-Based Privacy Preservation in Classification Problems. In Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), Houston, TX, USA, November 27-30, 2005. • K. Wang, B. C. M. Fung, and G. Dong. Integrating Private Databases for Data Analysis. In Proc. of the 2005 IEEE International Conference on Intelligence and Security Informatics (ISI 2005), pages 171-182, Atlanta, GA, USA, May 19-20, 2005. • B. C. M. Fung, K. Wang, and P. S. Yu. Top-Down Specialization for Information and Privacy Preservation. In Proc. of the 21st IEEE International Conference on Data Engineering (ICDE 2005), pages 205-216, Tokyo, Japan, April 5-8, 2005. For more information, visit http://www.cs.sfu.ca/~bfung