Utility-Based Anonymization for Continuous Data Publishing

Privacy preservation is an important issue in the release of data for mining purposes. In practical applications, data is published continuously as new data arrive. Recently, efficient anonymization for continuous data publishing has attracted much research work. However, a careful balance between privacy and utility for continuous data publishing remains an open problem. In this paper, we study the problem of utility-based anonymization for continuous data publishing. Armed with this utility metric, we will show how to make use of utility metric into anonymized tables. This information has an intuitive semantic meaning; it increases the utility beyond what is possible in the original k-anonymity and l-diversity frameworks. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.