A Data Sanitization Method for Privacy Preserving Data Re-publication
Joochang Lee, Hyuk Jin Ko, Eunju Lee, Wongil Choi, Ung Mo Kim · 2008
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, m-invariance model has been proposed to support re-publication of dynamic datasets. However, m-invariance model has two drawbacks. First, the m-invariant 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.