Privacy Preserving PageRank Algorithm By Using Secure Multi-Party Computation

TL;DR

This paper proposes a privacy-preserving PageRank algorithm utilizing secure multi-party computation and homomorphic encryption, enabling multiple parties to compute PageRank without revealing their private graph data.

Contribution

It introduces a novel method combining secure multi-party computation with homomorphic encryption for privacy-preserving PageRank calculation.

Findings

Secure multi-party PageRank computation achieved.

Data privacy maintained during distributed computation.

Homomorphic encryption effectively protects individual data.

Abstract

In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively using homomorphic encryption based on Paillier scheme. In the proposed PageRank computation, a user encrypt its own graph data using asymmetric encryption method, sends the data set into different parties in a privacy-preserving manner. Each party computes its own encrypted entity, but learns nothing about the data at other parties.