1 / 6

Privacy Preserving Data Mining

Privacy Preserving Data Mining. Benjamin Fung bfung(at)cs.sfu.ca. Privacy Preserving Data Mining. What is data mining?

reyesbest
Télécharger la présentation

Privacy Preserving Data Mining

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related