The Secure Link Prediction Problem
This addresses privacy concerns for data owners storing social network data in cloud servers, introducing a novel approach to secure link prediction.
The paper tackles the problem of predicting new interactions in social networks while keeping the graph data encrypted to protect sensitive information, presenting three algorithms that are formally proven secure and tested on real-life datasets.
Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers which reveals sensitive information about the network. The graphs are therefore stored in encrypted form. We study the link prediction problem on encrypted graphs. To the best of our knowledge, this secure link prediction problem has not been studied before. We use the number of common neighbors for prediction. We present three algorithms for the secure link prediction problem. We design prototypes of the schemes and formally prove their security. We execute our algorithms in real-life datasets.