PPCA: Privacy-preserving Principal Component Analysis Using Secure Multiparty Computation(MPC)

TL;DR

This paper introduces PPCA, a privacy-preserving PCA algorithm optimized for MPC platforms, achieving significant performance improvements and better training results with multi-party data.

Contribution

The paper presents a novel MPC-based PCA algorithm with algorithm-level optimizations, resulting in 200x faster performance while maintaining privacy guarantees.

Findings

Achieved 200x performance boost over existing methods.

Demonstrated improved training results with multi-party data.

Validated effectiveness on real-world datasets.

Abstract

Privacy-preserving data mining has become an important topic. People have built several multi-party-computation (MPC)-based frameworks to provide theoretically guaranteed privacy, the poor performance of real-world algorithms have always been a challenge. Using Principal Component Analysis (PCA) as an example, we show that by considering the unique performance characters of the MPC platform, we can design highly effective algorithm-level optimizations, such as replacing expensive operators and batching up. We achieve about 200$\times$ performance boost over existing privacy-preserving PCA algorithms with the same level of privacy guarantee. Also, using real-world datasets, we show that by combining multi-party data, we can achieve better training results.