Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption

Distributed sparse Gaussian process (dGP) models provide an ability to achieve accurate predictive performance using data from multiple devices in a time efficient and scalable manner. The distributed computation of model, however, risks exposure of privately owned data to public manipulation. In this paper we propose a secure solution for dGP regression models using multi-key homomorphic encryption. Experimental results show that with a little sacrifice in terms of time complexity, we achieve a secure dGP model without deteriorating the predictive performance compared to traditional non-secure dGP models. We also present a practical implementation of the proposed model using several Nvidia Jetson Nano Developer Kit modules to simulate a real-world scenario. Thus, secure dGP model plugs the data security issues of dGP and provide a secure and trustworthy solution for multiple devices to use privately owned data for model computation in a distributed environment availing speed, scalability and robustness of dGP.

Authors

• Adil Nawaz Shenzhen University
• Guopeng Chen Shenzhen University
• Muhammad Umair Raza Shenzhen University
• Zahid Iqbal Shenzhen University
• Jianqiang Li College of Computer Science and Software Engineering, Shenzhen University, China National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China
• Victor C.M. Leung Shenzhen University
• Jie Chen

Keywords

• ML: Bayesian Learning
• ML: Distributed Machine Learning & Federated Learning
• ML: Kernel Methods
• ML: Privacy