Protecting sensitive knowledge by data sanitization
Stanley Robson de Medeiros Oliveira, Osmar R. Zai͏̈ane · 2004
We address the problem of protecting some sensitive knowledge in transactional databases. The challenge is on protecting actionable knowledge for strategic decisions, but at the same time not losing the great benefit of association rule mining. To accomplish that, we introduce a new, efficient one-scan algorithm that meets privacy protection and accuracy in association rule mining, without putting at risk the effectiveness of the data mining per se.