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

arXiv: https://arxiv.org/abs/2105.07612 · HTML: https://mlsanity.com/paper/2105.07612/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2021.

## Built on by (most cited first, 2 of 2)

- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network](https://mlsanity.com/paper/2604.09091/) (2026)

## Abstract (arXiv)

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× 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.