Efficiently Anonymizing Social Networks with Reachability Preservation

The goal of graph anonymization is avoiding disclosure of privacy in social networks through graph modifications meanwhile preserving data utility of the anonymized graph for social network analysis. Graph reachability is an important data utility as reachability queries are not only common on graph databases, but also serving as fundamental operations for many other graph queries. However, the graph reachability is severely distorted after the anonymization. In this paper, we solve this problem by designing a reachability preserving anonymization (RPA for short) algorithm. The main idea of RPA is to organize vertices into groups and greedily anonymizes each vertex with low anonymization cost on reachability. We propose the reachable interval to efficiently measure the anonymization cost incurred by an edge addition, which guarantees the high efficiency of RPA. Extensive experiments illustrate that anonymized social networks generated by our methods preserve high utility on reachability.