Privacy-Preserving Protocols for Eigenvector Computation

TL;DR

This paper introduces a privacy-preserving protocol for computing the principal eigenvector of distributed data matrices using secure multi-party computation, homomorphic encryption, and an efficient power iteration method.

Contribution

It proposes a novel, efficient protocol combining secure computation and obfuscation techniques for eigenvector calculation across multiple parties.

Findings

Protocol ensures data privacy during eigenvector computation

Uses homomorphic encryption and randomization for security

Achieves efficiency over previous QR-based methods

Abstract

In this paper, we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy constraints. Our proposed protocol is based on secure multi-party computation with a semi-honest arbitrator who deals with data encrypted by the other parties using an additive homomorphic cryptosystem. We augment the protocol with randomization and obfuscation to make it difficult for any party to estimate properties of the data belonging to other parties from the intermediate steps. The previous approaches towards this problem were based on expensive QR decomposition of correlation matrices, we present an efficient algorithm using the power iteration method. We analyze the protocol for correctness, security, and efficiency.