Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Abstract EN

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data.

There are increasing concerns about the privacy problem in the frequent itemset mining.

Some works have been proposed to handle this kind of problem.

In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection.

To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique.

By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level.

Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.