A Unified Metric Method of Information Loss in Privacy Preserving Data Publishing

Data Publishing generates much concern over the protection of individual privacy. K-anonmization is a technique that prevents linking attacks by generalizing and suppressing portions of the released raw data so that no individual can be uniquely distinguished from a group of size of k. We study generalization for preserving privacy in publishing of sensitive data and metric method for information loss in process of generalization. In this paper, we provide a practical metric framework for implementing one model of k-anonymization, called generalization including suppression metric. We introduce Datafly algorithm for the metric method. Our experiments show that generalizatioin including suppression metric is more precision than those existing methods focusing on generalization.