Target link protection against link-prediction-based attacks via artificial bee colony algorithm based on random walk

Link prediction is a network analysis model used to discover missing links or future relationships that may appear, which has been widely used in many real network systems to predict the potential relationship between two individuals. However, link prediction can also be used by attackers to identify sensitive links that users are unwilling to expose, which makes only removing sensitive links from the original network ineffective and leads to the disclosure of privacy. In this paper, we propose a target link protection mechanism via artificial bee colony algorithm based on random walk (RABC), which can defend the link prediction attacks based on resource allocation (RA) metric effectively. To enhance the local search ability of RABC, the random walk algorithm is combined with the original artificial bee colony algorithm. Then, we compare our method with other existing methods, which shows that RABC has higher efficiency while ensuring the effectiveness. Finally, extensive experiments on real social networks are conducted to demonstrate the good performance of RABC on protecting sensitive links from being detected successfully by link prediction model. Furthermore, the perturbed networks generated by RABC is transferable to defend against other link prediction attacks.