Privacy Preserving Data Mining (PPDM)
In the light of developments in technology to analyze personal data,
public concerns regarding privacy are rising. While some believe that
statistical and Knowledge Discovery and Data Mining (KDDM)
research is detached from this issue, we can certainly
see that the debate is gaining momentum as KDDM and statistical
tools are more widely adopted by public and private organizations
hosting large databases of personal records.
One of the key requirements of a data mining project is access
to the relevant data.
Privacy and Security concerns can constrain such access, threatening
to derail data mining projects.
This workshop will bring together researchers and practitioners to
identify problems and solutions where data mining interferes with
privacy and security.
Topics of Interest
Learning from perturbed/obscured data.
Techniques for protecting confidentiality of sensitive information,
including work on statistical databases,
and obscuring or restricting data access to prevent violation of
privacy and security policies.
Learning from distributed data sets with limits on sharing of
Hiding knowledge in data sets.
Underlying methods and techniques to support data mining while respecting
privacy and security (e.g., secure multi-party computation).
The relationship between privacy and knowledge discovery,
and algorithms for balancing privacy and knowledge discovery.
Meanings and measuring of ``privacy'' in privacy-preserving data mining.
Use of data mining results to reconstruct private information, and
corporate security in the face of analysis by KDDM and statistical
tools of public data by competitors.
Use of anonymity techniques to protect privacy in data mining.
|Intent to submit|
(appreciated, not required)
|August 22, 2003|
|Paper submission||September 5, 2003 (extended)|
|Notification of acceptance||September 26, 2003|
| Camera ready papers||October 10, 2003|
| Workshop date||November 19, 2003|
Wenliang (Kevin) Du (Chair)
Department of Electrical Engineering and Computer Science
Syracuse, NY 13244 USA
Tel: +1 315-443-9180, Fax: +1 315-443-1122
Chris Clifton (Co-Chair)
Department of Computer Sciences
West Lafayette, Indiana 47907-1398 USA
Tel: +1 765-494-6005, Fax: +1 765 494-0739
- Wesley Chu, University of California, Los Angeles
- George Cybenko, Dartmouth College
- Vladimir Estivill-Castro, Griffith University
- Johannes Gehrke, Cornell University
- Tom Johnsten, University of South Alabama
- Hillol Kargupta, University of Maryland Baltimore County
- Stanley R. M. Oliveira, Embrapa Information Technology
- Benny Pinkas, Trusted Systems Lab, HP Labs
- Vijay V. Raghavan, University of Louisiana Lafayette