GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning

GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning

Qiao Zhang, Cong Wang, Hongyi Wu, Chunsheng Xin, Tran V. Phuong

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3933-3939. https://doi.org/10.24963/ijcai.2018/547

Privacy is a fundamental challenge for a variety of smart applications that depend on data aggregation and collaborative learning across different entities. In this paper, we propose a novel privacy-preserved architecture where clients can collaboratively train a deep model while preserving the privacy of each client’s data. Our main strategy is to carefully partition a deep neural network to two non-colluding parties. One party performs linear computations on encrypted data utilizing a less complex homomorphic cryptosystem, while the other executes non-polynomial computations in plaintext but in a privacy-preserved manner. We analyze security and compare the communication and computation complexity with the existing approaches. Our extensive experiments on different datasets demonstrate not only stable training without accuracy loss, but also 14 to 35 times speedup compared to the state-of-the-art system.
Keywords:
Multidisciplinary Topics and Applications: Security and Privacy
Machine Learning: Deep Learning