Prevention of walk based attack on Social Network graphs using Ant Colony Optimization

Social network is one of the most impactful innovations of the last decade. It gives a way to connect millions of people around the world. Social networking sites sometimes sell their data to third party organizations for analysis and data mining, as a result of which, there is a chance that privacy of the users are compromised. Even after naive anonymization of the social graph, several attacks are possible to identify the victim and hence his private information can be extracted. Walk based attacks are one of the most prominent active attacks on a social networking graph. Where the attacker creates a set of malicious nodes before naive anonymization and attaches them to a target node creating an identifiable subgraph. Then in the naive anonymized graph it tries to identify the subgraph, if it can do so then the identity of the victim is compromised. We have proposed an algorithm that prevents walk based attacks to a large extent yet minimizing the data loss thus retaining the data mining quality of the social graph to a considerable extent. We have used “user characteristics metric” along with Ant Colony Optimization to anonymize our data maintaining the aforesaid criteria.