A Novel Approach for Hiding High Utility Sequential Patterns
Duy-Tai Dinh, Minh Nguyen Quang, Bac Hoai Le · 2015
Privacy Preserving Data Mining (PPDM) has become an important research topic in recent years. Hiding high utility sequential patterns is very necessary in business, health and security applications, etc. The goal of hiding is to find the way to hide all high utility sequential patterns so that the adversaries cannot mine them from the sanitized database. However, there are a few methods in the literature for hiding high utility sequential patterns. In this paper, we present a new approach for solving this problem. First, we use an expansion algorithm of USpan [2] for mining all high utility sequential patterns. Then we present two proposed algorithms HHUSP (Hiding High Utility Sequential Pattern) and MSPCF (Maximum Sequential Patterns Conflict First) to hide all high utility sequential patterns. Experimental results show the evaluation of execution time, memory usage on the large-scale datasets.