Permutation Anonymization: Improving Anatomy for Privacy Preservation in Data Publication

Abstract

Anatomy is a popular technique for privacy preserving in data publication. However, anatomy is fragile under background knowledge attack and can only be applied into limited applications. To overcome these drawbacks, we develop an improved version of anatomy: permutation anonymization, a new anonymization technique that is more effective than anatomy in privacy protection, and meanwhile is able to retain significantly more information in the microdata. We present the detail of the technique and build the underlying theory of the technique. Extensive experiments on real data are conducted, showing that our technique allows highly effective data analysis, while offering strong privacy guarantees.