Enhancing Privacy of Released Database
Tingting Chen, Sheng Zhong · 2007 IEEE International Conference on Granular Computing (GRC 2007) · 2007
With advanced information techniques, organizations want to make their database public for different purposes. It is important to do some data transformations that prevent private information to be revealed before publishing the database. In this paper, we introduce a combined approach to enhance the privacy of the databases to be released. The combination of two existing techniques, k-anonymity and randomization, provides better privacy protection than only applying one of two approaches and still reserves certain data utility. The experiments on real-world dataset show that our privacy breach prevention algorithm enhances the privacy with small cost increase compared to the k-anonymity approach.