Blocking Inference Channels in Frequent Pattern Sharing

The knowledge discovered by frequent pattern mining is represented in the form of a collection of frequent patterns with their supports. Sharing the frequent patterns without discrimination may bring threats against privacy and security, because some of frequent patterns themselves may be sensitive and should not be disclosed. Furthermore, due to the existence of inference channels, an attacker may also derive sensitive patterns from a set of non-sensitive patterns. Therefore, just eliminating sensitive patterns from the mining result is not enough to prevent their disclosure. We classify the potential inference channels into three categories, and present two algorithms for blocking these inference channels by pattern sanitization. The main advantage of our work is that it does not bring about any fake knowledge, and also does not distort the original knowledge.