MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining

Data mining mechanisms have widely been applied in various businesses and manufacturing companies across many industry sectors. Sharing data or sharing mined rules has become a trend among business partnerships, as it is perceived to be a mutually benefit way of increasing productivity for all parties involved. Nevertheless, this has also increased the risk of unexpected information leaks when releasing data. To conceal restrictive itemsets (patterns) contained in the source database, a sanitization process transforms the source database into a released database that the counterpart cannot extract sensitive rules from. The transformed result also conceals non-restrictive information as an unwanted event, called a side effect or the “misses cost.” The problem of finding an optimal sanitization method, which conceals all restrictive itemsets but minimizes the misses cost, is NP-hard. To address this challenging problem, this study proposes the Maximum Item Conflict First (MICF) algorithm. Experimental results demonstrate that the proposed method is effective, has a low sanitization rate, and can generally achieve a significantly lower misses cost than those achieved by the MinFIA, MaxFIA, IGA and Algo2b methods in several real and artificial datasets.