关键词: 隐私保护, 数据发布, 变长聚类, 个性化匿名, 信息损失

Abstract: For the privacy disclosure caused by linking attack, while in order to reduce the information loss of anonymous protection and improve the availability of published data sets, this paper presents a personalization anonymous anonymization protection method based on variable-length clustering for individual. The method fully considers the record weights on the result of clustering cluster-center to achieve better availability of data, and classification processing of sensitive attribute values. The sensitive attribute values are divided into three levels, meeting the protection requirements of different individuals. Theoretical analysis and experimental results show that the method can meet the personalized requirements to protect sensitive attributes, effectively reducing the loss of information at the same time, and with high efficiency. The generated anonymous data set has good availability.

Key words: privacy protection, data publishing, variable-length clustering, personalization anonymization, information loss