A Model of Privacy Preserving in Dynamic Set-valued Data Re-publication

Abstract

A model of privacy preserving in dynamic set-valued data re-publication is studied in this paper. Dynamic data in most practical applications may be re-published after updating, and the sensitive information of which may confront the risk of being exposed by adversary using historical publish results. A novel k-preserving model is proposed to protect data privacy from being exposed by continuing using transactional k-anonymity and maintaining the diversity and continuity of sensitive elements in the dataset during re-publication. An anonymous algorithm is also proposed to reduce information loss of the anonymous result by integrating local generalization with suppression technique. Real-world datasets are used in the experiment, the results and evaluations demonstrate that the approach in this paper can prevent privacy disclosure effectively and acquire publishing result with better availability.