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Joint Biostatistics & Medical Informatics / Statistics Seminar

Turning Data into Knowledge Without Violating Privacy

Chris Clifton, PhD, Associate Professor,
Dept. of Computer Science, Purdue University

Thursday, January 29, 2004, 4-5 p.m.

1221 Computer Sciences and Statistics, 1210 W. Dayton St.

ABSTRACT

The confluence of data mining, large databases, and networked information sources opens a wealth of possibilities for knowledge discovery. Privacy and security concerns have lead to a backlash against these technologies, witness street protests in Japan in August 2002 over the creation of a national registry and ID number, and the introduction in the U.S. Senate of the "Data-Mining Moratorium Act of 2003".

The irony is that most data mining generates summary results that do not violate privacy. Are we simply facing a need to educate the public on what data mining really is? The answer is no: the problem is real. It isn't data mining that is at fault, but gathering the data into a common warehouse to enable data mining. In general, problem arise when data must be shared.

This talk explores how privacy-preserving data mining and other privacy-preserving collaboration techniques can enable applications that might otherwise be prevented due to privacy concerns. Examples will be presented from both public and private sector, with an emphasis on medical applications and solutions that are (and aren't) likely to be compliant with the Healthcare Information Portability and Accountability Act (HIPAA).

 

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