Frequent Itemsets Mining with a Guaranteed Local Differential Privacy in Small Datasets
Sharmin Afrose, Tanzima Hashem, Mohammed Eunus Ali · 2021
In this paper, we propose an iterative approach to estimate the frequent itemsets with high accuracy while satisfying the local differential privacy (LDP). The key component behind the improved accuracy of the estimated frequent itemsets by our approach is our novel two-level randomization technique for guaranteeing the LDP. Our randomization technique exploits the correlation of the presence of items in a user’s itemset, which has not been considered before. We present a mathematical proof that shows that our approach satisfies the LDP constraint. Extensive experiments are performed to validate the effectiveness and efficiency of our proposed algorithms using real datasets.