Implementation of Privacy-preserving SimRank over Distributed Information Network

TL;DR

This paper presents a privacy-preserving protocol for computing node similarity in distributed information networks using fully-homomorphic encryption, enabling collaborative analysis without compromising data privacy.

Contribution

It introduces a novel privacy-preserving SimRank protocol that leverages fully-homomorphic encryption for secure distributed network analysis.

Findings

Secure similarity computation over distributed networks

Protection of link data privacy using cryptography

Effective collaboration without data leakage

Abstract

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.