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

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

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. Realworld 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.