A QI Weight-Aware Approach to Privacy Preserving Publishing Data Set

In recent years, publishing data about individuals without revealing their identity information has become an active issue, and k-anonymity based models are the effective techniques that can prevent linking attacks. Most of the previous works, however, focus on the efficiency and the scope of application of the models. Specific requirements of quality of published microdata for the analyzing task in various scenarios and the difference of contributions of each QI attribute to the result have not been addressed. If the contribution of different generalizing paths and orders of QI attributes has not been considered, the published microdata may have bad utility in the application. Paying more attention to them, which makes the published table have different utility, is valuable. By analyzing the differences among several application areas, a scheme which provides an effective and secure tradeoff of privacy and utility, is proposed. Firstly the basic ODP is revised to indicate the characters of special domain. Secondly, the weight on quasi-attribute is introduced to reflect the effect for the data analyzing task. And then QI weight-aware k-anonymity (WAK), which is an algorithm based on the weight of attribute, is introduced. Theoretical analysis and experimental results testify that the scheme is effective and can preserve privacy of the sensitive data well, meanwhile maintaining better data utility.