Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption
Adil Nawaz, Guopeng Chen, Muhammad Umair Raza, Zahid Iqbal, Jianqiang Li, Victor C.M. Leung, Jie Chen
Abstract
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.
BibTeX
@article{Nawaz_Chen_Raza_Iqbal_Li_Leung_Chen_2024, title={Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29357}, DOI={10.1609/aaai.v38i13.29357}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Nawaz, Adil and Chen, Guopeng and Raza, Muhammad Umair and Iqbal, Zahid and Li, Jianqiang and Leung, Victor C.M. and Chen, Jie}, year={2024}, month={Mar.}, pages={14431-14439} }