Community-preserving anonymization of graphs

摘要

In this paper, we propose a novel edge modification technique that better preserves the communities of a graph while anonymizing it. By maintaining the core number sequence of a graph, its coreness, we retain most of the information contained in the network while allowing changes in the degree sequence, i. e. obfuscating the visible data an attacker has access to. We reach a better trade-off between data privacy and data utility than with existing methods by capitalizing on the slack between apparent degree (node degree) and true degree (node core number). Our extensive experiments on six diverse standard network datasets support this claim. Our framework compares our method to other that are used as proxies for privacy protection in the relevant literature. We demonstrate that our method leads to higher data utility preservation, especially in clustering, for the same levels of randomization and k-anonymity.