Anonymization of Quasi-Sensitive Attribute Sets in Aggregated Dataset

Abstract

The widespread use of Internet of Things (IoT) and Data Fusion technologies make privacy protection an urgent problem to be solved. The aggregated datasets generated in these two scenarios face extra privacy disclosure. We define attribute sets with different sources in an aggregated dataset as quasi-sensitive attribute sets (QS sets). The QS set itself is not sensitive, but internal linking attacks may occur when two QS sets in an aggregated dataset are linked. In this paper, we propose a new privacy model, namely, the QS k-anonymity model. The QS k-anonymity model is effective in preventing internal linking attacks. We provide two algorithms for the QS k-anonymity model, the Greedy QS k-anonymity algorithm and the Efficient QS k-anonymity algorithm. We evaluate our algorithms on real datasets. The experimental results show that the Greedy QS k-anonymity algorithm has good data utility, the Efficient QS k-anonymity algorithm shows better efficiency, and both algorithms are well scalable.