Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining

Abstract

Social network data provide valuable information for companies to better understand the characteristics of their potential customers with respect to their communities. Yet, sharing social network data in its raw form raises serious privacy concerns because a successful privacy attack not only compromises the sensitive information of the target victim but also divulges the relationship with his/her friends or even their private information. In recent years, several anonymization techniques have been proposed to solve these issues. Most of them focus on how to achieve a given privacy model but fail to preserve the data mining knowledge required for data recipients. In this paper, we propose a method to $$k$$k-anonymize a social network dataset with the goal of preserving frequent sharing patterns and maximal frequent sharing patterns, the most important kinds of knowledge required for marketing and consumer behavior analysis. Experimental results on real-life data illustrate the trade-off between privacy and utility loss with respect to the preservation of (maximal) frequent sharing patterns.