Implementation of Privacy-preserving SimRank over Distributed Information Network
This addresses privacy concerns for parties cooperating to improve similarity measures in distributed networks, though it appears incremental as it applies an existing encryption method to a known problem.
The paper tackles the problem of computing node similarity in distributed information networks while preserving data privacy, proposing a privacy-preserving SimRank protocol based on fully-homomorphic encryption to protect link data.
Information network analysis has drawn a lot attention in recent years. Among all the aspects of network analysis, similarity measure of nodes has been shown useful in many applications, such as clustering, link prediction and community identification, to name a few. As linkage data in a large network is inherently sparse, it is noted that collecting more data can improve the quality of similarity measure. This gives different parties a motivation to cooperate. In this paper, we address the problem of link-based similarity measure of nodes in an information network distributed over different parties. Concerning the data privacy, we propose a privacy-preserving SimRank protocol based on fully-homomorphic encryption to provide cryptographic protection for the links.