As a representative model for privacy preserving data publishing, K-anonymity has raised a considerable number of questions for researchers over the past few decades. Among them, how to achieve data release without sacrificing the users’ privacy and how to maximize the availability of published data is the ultimate goal of privacy preserving data publishing. In order to enhance the clustering effect and reduce the unnecessary computation, this paper proposes a weighted K-member clustering algorithm. A series of weight indicators are designed to evaluate the outlyingness of records, distance between records, and information loss of the published data. The proposed algorithm can reduce the influence of outliers on the clustering effect and maintain the availability of data to the best possible extent during the clustering process. Experimental analysis suggests that the proposed method generates lower information loss, improves the clustering effect, and is less sensitive to outliers as compared with some existing methods.

作为隐私保护数据发布的代表模型，过去几十年来，K-匿名性为研究人员提出了很多问题。其中，如何在不牺牲用户隐私的情况下实现数据发布以及如何最大化已发布数据的可用性是隐私保护数据发布的最终目标。为了提高聚类效果并减少不必要的计算量，提出了一种加权的K元聚类算法。设计了一系列权重指标来评估记录的外在性，记录之间的距离以及已发布数据的信息丢失。提出的算法可以减少离群值对聚类效果的影响，并在聚类过程中最大程度地保持数据的可用性。