A novel clustering-based anonymization approach for graph to achieve Privacy Preservation in Social Network

摘要

Serious privacy concern rises with the prosperity of social network applications. To prevent the privacy of vertices or edges associated with entities in a social network from getting re-identified through background information or queries, a novel clustering-based approach is proposed to anonymize vertices and edges. Concepts of vertex similarity matrix and the distance between a vertex and a cluster are defined, based on which a k -anonymized graph approach is presented. The effectiveness of the approach is verified Through experiments that compare the performance of our method with that of SASN, an existing anonymization algorithm.