Privacy Protection Algorithm for Frequent Itemset Mining

Abstract

A differentially private frequent itemset mining algorithm DP-FMA is proposed for privacy protection of frequent itemset mining. The existing algorithms have great damage to the data. In order to solve this problem, DP-FMA is based on the real frequent itemset to mine the frequent itemset with noise, so that the support of frequent itemset will not decrease, and the availability of mining results will be improved. Aiming at the problem of inconsistency between the support with noise and the real support for mining in existing algorithms, a consistent constraint strategy is proposed, which makes the mining support with noise in descending order of integer and ultimately improves the accuracy of mining results. Finally, theoretical analysis is used to prove the differential privacy and availability of the algorithm. The experimental comparison proves the high availability and high accuracy of the algorithm.