Improving the Effect of Frequent Itemset Mining with Hadamard Response under Local Differential Privacy

Abstract

Frequent itemset mining is a basic data mining task and has many applications in other data mining tasks. However, it is likely that the user's personal privacy information may be leaked in the mining process. In recent years, applying the local differential privacy protection model to mine all frequent itemsets is a relatively reliable and secure protection method. In local differential privacy, users first perturb the original data then send it to the aggregator, which prevents the aggregator leaking user's private information throughout the process. There are two major problems with data mining using local differential privacy, one is that the accuracy of the results is relatively low after mining, and the other is that the user transmits too much data to the server, which results in higher communication costs. In this paper, we use Hadamard response algorithm to improve the accuracy of the results while reduce the communication cost. Finally, we use FP-tree for frequent itemset mining to compare the Hadamard response with previous algorithms.