# The Secure Link Prediction Problem

## TL;DR

This paper introduces the first study of secure link prediction on encrypted graphs, proposing three algorithms, proving their security, and testing them on real datasets to protect sensitive network information.

## Contribution

It presents the first algorithms for link prediction on encrypted graphs, with formal security proofs and practical implementations.

## Key findings

- Algorithms successfully predict links on encrypted data
- Security of the schemes is formally proven
- Real-life dataset experiments demonstrate practicality

## Abstract

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.