# Privacy Preserving PCA for Multiparty Modeling

arXiv: https://arxiv.org/abs/2002.02091 · HTML: https://mlsanity.com/paper/2002.02091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Federated singular value decomposition for high dimensional data](https://mlsanity.com/paper/2205.12109/) (2022)

## Abstract (arXiv)

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data. PPPCA can accomplish multiparty cooperative execution of PCA under the premise of keeping plaintext data locally. We also propose implementations using two techniques, i.e., homomorphic encryption and secret sharing. The output of PPPCA can be sent directly to data consumer to build any machine learning models. We conduct experiments on three UCI benchmark datasets and a real-world fraud detection dataset. Results show that the accuracy of the model built upon PPPCA is the same as the model with PCA that is built based on centralized plaintext data.