Secure and effective anonymization against re-publication of dynamic datasets

Current researches of privacy preserving data publication concentrate on static dataset which have no updates. However, most of the real world data sources are dynamic. Applying the existing static dataset privacy preserving techniques directly causes unexpected private information disclosure frequently. Few literatures relate to the serial data publication on dynamic datasets meanwhile there are some deficiency in these recent researches. This paper discusses exhaustively various inference channels of serial releasing dynamic datasets on medical records, and then proposes an efficient algorithm on the idea of “invariance”. The experimental results show that our method protects privacy adequately and has low information loss metric.