A Data Sanitization Method for Privacy Preserving Data Re-publication

When a table containing personal information is published, sensitive information should not be revealed Although k-anonymity and l-diversity models are popular approaches to protect privacy, they are limited to one time data publishing. After a dataset is updated with insertions and deletions, a data holder cannot safely release up-to-date information. Recently, minvariance model has been proposed to support republication of dynamic datasets. However, minvariance model has two drawbacks. First, the minvariant generalization can cause high information loss. Second, if the adversary already obtained sensitive values of some individuals before accessing released information, m-invariance leads to severe privacy breaches. In this paper, we propose a new data sanitization technique for safely releasing dynamic datasets. The proposed technique prevents two drawbacks of m-invariance and provides a simple and effective method for handling inserted and deleted records.