When the Attacker Knows a Lot: The GAGA Graph Anonymizer

Abstract

When releasing graph data (e.g., social network) to public or third parties, data privacy becomes a major concern. It has been shown that state-of-the-art graph anonymization techniques suffer from a lack of strong defense against De-Anonymization (DA) attacks mostly because of the bias towards utility preservation. In this paper, we propose GAGA, an Efficient Genetic Algorithm for Graph Anonymization, that simultaneously delivers high anonymization and utility preservation. To address the vulnerability against DA attacks especially when the adversary can re-identify the victim not only based on some information about the neighbors of a victim but also some knowledge on the structure of the neighbors of the victim’s neighbors, GAGA puts the concept of k(d)-neighborhood-anonymity into action by developing the first general algorithm for any d distance neighborhood. GAGA also addresses the challenge of applying minimum number of changes to the original graph to preserve data utilities via an effective and efficient genetic algorithm. Results of our evaluation show that GAGA anonymizes the graphs in a way that it is more resistant to modern DA attacks than existing techniques – GAGA (with d = 3) improves the defense against DA techniques by reducing the DA rate by at least a factor of 2.7\(\times \) in comparison to the baseline. At the same time it preserves the data utilities to a very high degree – it is the best technique for preserving 11 out of 16 utilities. Finally, GAGA provides application-oriented level of control to users via different tunable parameters.