Privacy-preserving utility itemset mining is the process of hiding sensitive-high utility itemsets (SHUIs) appearing in original database such that they will not be discovered in the sanitized database. The purpose of SHUI hiding algorithm is to conceal the set of SHUIs while minimizing the side effects which caused by data distortion process. In this paper, a novel algorithm, named EHSHUI (An Efficient Algorithm for Hiding Sensitive-high utility Itemsets), is proposed to minimize the side effects of the sanitization process. The proposed algorithm includes three heuristic steps: (1) The transaction on which the SHUI achieves maximal utility among transactions containing it is specified as victim transaction; (2) The item that causes minimal impacts on non-SHUIs is selected as victim item; and (3) An exactly number of utility is calculated for reducing internal utility of victim item from victim transaction. This strategy exactly identifies item and transaction for data modification such that it minimizes the impacts on non-SHUIs, data distortions, and the time to access database. The experiment results illustrate that the proposed algorithm achieves higher performance and lower side effects than the state-of-the-art.

中文翻译：

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隐藏敏感的高实用项集的有效算法

隐私保护实用程序项集挖掘是隐藏出现在原始数据库中的敏感度高的实用程序项集（SHUI）的过程，这样就不会在经过清理的数据库中发现它们。SHUI隐藏算法的目的是隐藏SHUI集，同时最大程度地减少由数据失真过程引起的副作用。本文提出了一种新的算法，称为EHSHUI（一种隐藏敏感的高实用项集的有效算法），以最大程度地减少消毒过程的副作用。该算法包括三个启发式步骤：（1）将SHUI在其中获得最大效用的交易指定为牺牲交易；（2）选择对非SHUI影响最小的项目作为受害者项目；（3）计算效用的确切数目，以减少受害者交易中受害者物品的内部效用。该策略准确地标识了用于数据修改的项目和事务，从而最大程度地减少了对非SHUI，数据失真和访问数据库的影响。实验结果表明，与现有技术相比，该算法具有更高的性能和更低的副作用。