Differential Privacy Frequent Closed Itemset Mining over Data Stream

As the most common technique for mining and analyzing massive data, frequent itemset mining is widely used in various scenarios. However, when the data contains sensitive information, it will bring serious privacy leakage risks to mining and publishing it directly. Therefore, how to efficiently mine frequent itemset without privacy disclosure is a hot issue at present. In data stream, because the data between adjacent timestamps possess certain correlation, which makes it easier to leak privacy for frequent itemset mining in the data stream, and considering that frequent itemset will have combinatorial explosion problem in data stream, frequent closed itemset mining in the data stream will be a better choice. In this paper, we propose a differential privacy algorithm for mining frequent closed itemset in data stream, which is referred to as DPES. The algorithm uses a vertical mining method in each sliding window, which can quickly obtain the frequent closed itemset contained in the current window. Besides, to improve the data utility, we also design an adaptive privacy budget allocation strategy by computing the difference to decide whether the current timestamp should publish a low-noise statistic or an approximate statistic. Finally, we demonstrate that the DPES algorithm satisfies ε-difference privacy by privacy analysis, and the experimental results on several real datasets also show the effectiveness of the DPES algorithm.