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.
Note
The 8th ACM SIGMOD
Workshop on Research Issues in Data Mining and Knowledge Discovery
(DMKD'2003)
June 13, 2003 in San Diego, California
