Anonoymizing Methods against Republication of Incremental Numerical Sensitive Data

Privacy protection for numerical sensitive data has become a serious concerned in many applications. Current privacy protection for numerical sensitive data base on the static datasets. However, most of the real world data sources are dynamic, and the direct application of the existing static datasets privacy preserving techniques often causes the unexpected private information disclosure. This paper anaylisis various leakage risks of republication of incremental numerical sensitive data on numerical sensitive data, and proposes an efficient algorithm on anonoymizing methods against republication of incremental numerical sensitive data,The experiments show that this method protects privacy adequately.