Enhanced anonymous models for microdata release based on sensitive levels partition

Abstract

Depending on the inherent characteristic of sensitive attributes, even those existing enhanced anonymous models still permit the private information to be disclosed or have other limitations so far, such as skewness attacks or sensitive attacks. Based on this, three novel enhanced anonymous models, i.e., (p,αisg)-sensitive k-anonymity model, (p+,αisg)-sensitive k-anonymity model and (pi+,αisg)-sensitive k-anonymity model, are proposed to improve the privacy preservation by fully considering the sensitivity of different sensitive values on the sensitive attribute so as to realize better personalized privacy preservation. Different from the traditional methods, which basically quantify the sensitive level of the specific sensitive value focusing on user-defined classification approaches, the sensitivity of different sensitive values based on self-information is fully considered to obtain sensitive levels partition (SLP) so as to achieve better privacy by designing SLP from qualitative status towards quantitative status in our models. The conception of identical sensitive group (ISG), which is generated from the idea of hierarchical clustering method by using SLP algorithm, is introduced to design the anonymous models for better defending against sensitive attacks. In this case, the sensitive values with the most similar sensitivity are most likely clustered in the same ISG. Moreover, the frequency of each ISG is confined to no more than the specific personalized threshold αisg in any equivalence class without ending up with “one size for all” restrictions. Here, higher sensitive of ISG should be assigned a lower frequency constraint for resisting sensitive attacks so as to achieve better privacy. Then, two clustering algorithms are devised by using the idea of bottom-up greedy methods. Experiment results based on two real-world datasets show that our three anonymous models can effectively protect data privacy and enhance data security and practicality with certain information loss.