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Published in:
2016 | OriginalPaper | Chapter
A Sanitization Approach of Privacy Preserving Utility Mining
Authors : Jerry Chun-Wei Lin, Tsu-Yang Wu, Philippe Fournier-Viger, Guo Lin, Tzung-Pei Hong, Jeng-Shyang Pan
Published in: Genetic and Evolutionary Computing
Publisher: Springer International Publishing
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High-Utility Itemset Mining (HUIM) considers both quantity and profit factors to measure whether an item or itemset is a profitable product. With the rapid growth of security considerations, privacy-preserving utility mining (PPUM) has become a critical issue in HUIM. In this paper, an efficient algorithm is proposed to minimize side effects in the sanitization process for hiding sensitive high utility itemsets. Three similarity measurements are also designed as the new standard used in PPUM. Experiments are also conducted to show the performance of the designed algorithm in terms of general side effects in PPDM and the new defined measurements in PPUM.