A Hellinger Distance Based Anonymization Method for Weighted Social Networks

Rapid development of web 2.0 and social networks brings convenience for users´ information sharing. The thriving of information sharing inevitably leaves users´ private information vulnerable to leakage. Weighted social networks can provide more personal information than unweighted social networks, such as those weight based information. Weight based information (weight distribution, shortest paths etc.) can be not only the objects needing preserving but also clues grasped by adversaries to initiate identity re-identification. Most of current work however overlooks this kind of privacy leakage incurred by adversaries´ grasping of some weight distribution information. In this paper, we propose a Hellinger distance based privacy model (k, λ)-similarity to surmount the problem of sensitive identity re-identification leveraging background knowledge of weight distribution. Particularly, a sliding window and binary approximation based data perturbation algorithm SWBADP is devised to realize (k, λ)-similarity privacy constraint. Further, concerning the potential privacy leakage originated from merely k-degree anonymous, an optimization criterion, called depth clustering, is discussed to address the problem. The empirical studies demonstrate our implementation delivers both well defense capability to sensitive identity re-identification and better weight based data utility.