关键词: 迭代优化, 二分聚类, 隐私保护, K匿名, 概化

Abstract: With the deepening of data sharing in various fields, the protection of individual privacy contained in data has become increasingly prominent. At the same time, Kanonymity, as an advanced theory of privacy protection, is also widely used in data sharing and distribution. However, Kanonymity, as a way to achieve privacy protection by generalizing data, will inevitably cause a certain loss of information. Therefore, how to ensure data availability and reduce the information loss as much as possible under the premise of satisfying Kanonymity is a question worthy of study. For this problem, for numerical data, a Kanonymity algorithm KABIBC (Kanonymous algorithm based on iterative binary clustering) based on iterative binary clustering is proposed to achieve Kanonymity. First, the sum of the distances within the group is defined, i.e., WGSD(withingroup sum of distance), and treat all tuples in the data table as a cluster, and then use an iterative strategy to perform binary clustering on it, and recursively process the obtained subclusters in the same way,  and reasonably adjust the tuple assignment of the two subclusters based on the principle of minimizing the information loss in the bisection, until the minimum subcluster that satisfies the Kanonymity requirement is obtained, so as to ensure that the amount of information loss tends to be optimal. Theoretical and experimental analysis are given, and it is shown that this mechanism can effectively reduce the information loss, and at the same time has a high operating efficiency.

Key words: iterative optimization, binary clustering, privacy protection, Kanonymity, generalization