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.
@article{arxiv.2002.02091,
title = {Privacy Preserving PCA for Multiparty Modeling},
author = {Yingting Liu and Chaochao Chen and Longfei Zheng and Li Wang and Jun Zhou and Guiquan Liu and Shuang Yang},
journal= {arXiv preprint arXiv:2002.02091},
year = {2020}
}