With growing concern of data privacy violations, privacy preservation processes become more intense. The k-anonymity method, a widely applied technique, transforms the data such that the publishing datasets must have at least k tuples to have the same link-able attribute, quasi-identifiers, values. From the observations, we found that, in a certain domain, all quasi-identifiers of the datasets, can have the same data type. This type of attribute is considered as an Identical Generalization Hierarchy (IGH) data. An IGH data has a particular set of characteristics that could utilize for enhancing the efficiency of heuristic privacy preservation algorithms. In this paper, we propose a data privacy preservation heuristic algorithm on IGH data. The algorithm is developed from the observations on the anonymous property of the problem structure that can eliminate the privacy constraints consideration. The experiment results are presented that the proposed algorithm could effectively preserve data privacy and also reduce the number of visited nodes for ensuring the privacy protection, which is the most time-consuming process, compared to the most efficient existing algorithm by at most 21%.

随着对数据隐私侵犯的日益关注，隐私保护过程变得更加激烈。该ķ -anonymity法，广泛应用的技术，转换成数据，使得数据集发布必须具有至少ķ元组具有相同的链路能属性，准标识符值。从观察中我们发现，在某个域中，数据集的所有准标识符都可以具有相同的数据类型。这种类型的属性被视为相同的泛化层次 ( IGH ) 数据。一个IGH数据具有特定的一组可以利用增强的启发式隐私保护算法的效率特征。在本文中，我们提出了一种数据隐私保护启发式算法IGH数据。该算法是根据对问题结构的匿名属性的观察而开发的，可以消除隐私约束的考虑。实验结果表明，所提出的算法可以有效地保护数据隐私，并减少访问节点的数量，以确保隐私保护，这是最耗时的过程，与现有算法相比最多减少 21%。