Assuring privacy when big brother is watching
Download
Author
Christopher Clifton
Tech report number
CERIAS TR 2003-46
Entry type
inproceedings
Abstract
Homeland security measures are increasing the amount of data collected, processed and mined. At the same time, owners of the data raised legitimate concern about their privacy and potential abuses of the data. Privacy-preserving data mining techniques enable learning models without violating privacy. This paper addresses a complementary problem: What if we want to apply a model without revealing it? This paper presents a method to apply classification rules without revealing either the data or the rules. In addition, the rules can be verified not to use "forbidden" criteria.
Download
Date
2003 – 06
Address
San Diego, CA
Key alpha
Clifton
Note
The 8th ACM SIGMOD
Workshop on Research Issues in Data Mining and Knowledge Discovery
(DMKD'2003)
June 13, 2003 in San Diego, California
Publication Date
2003-06-01