Towards Privacy Preserving Data Publishing∗

High quality and useful knowledge is to be found in the integrated data from various organizations, and the discovered knowledge is essential for building intelligent systems such as business analysis and health surveillance. However, concern about breaching privacy is a major obstacle of this process. This project aims to develop new efficient and effective techniques for privacy protection in data sharing and data mining by combining techniques in data mining and security research. We focus primarily on notions of anonymity that are defined with respect to individual identity, or with respect to the value of a sensitive attribute. Our goal is to propose a variety of techniques to anonymize original data sets, while preserving the utility of the input data. We adopt extensive evaluations to indicate that it is possible to distribute highquality data that respects several meaningful notions of privacy. Further, it is possible to do this efficiently for large transactional data sets. The developed cutting edge techniques will advance and facilitate data mining within many organizations and businesses and lead to the better utilization of information.