An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint
Jimmy Ming‐Tai Wu, Gautam Srivastava, Unil Yun, Shahab Tayeb, Jerry Chun‐Wei Lin · Transactions on Emerging Telecommunications Technologies · 2021
Abstract Privacy‐preserving data mining (PPDM) is a popular research topic in the data mining field. For individual information protection, it is vital to protect sensitive information during data mining procedures. Furthermore, it is also a serious offense to spill sensitive private knowledge. Recently, many PPDM data mining algorithms have been proposed to conceal sensitive items in a given database to disclose high‐frequency items. These recent methods have already proven to be excellent in protecting confidential information and maintaining the integrity of the input database. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. If a sensitive itemset includes more items, it will cause it the become more likely to be found. Before performing mining processes, a fixed value of the minimum support threshold will be set. In this paper, a new concept of minimal support for solving this issue is proposed. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. The results of the experiments show the performance of the traditional Greedy PPDM approach, Genetic algorithm (GA)‐based PPDM approaches, and the proposed particle swarm optimization‐based algorithm with the new minimal support function. The results show that the proposed method performs similarly to conventional algorithms and offers higher protection than previous methods.