Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption
DOI:
https://doi.org/10.1609/aaai.v38i13.29357Keywords:
ML: Privacy, ML: Distributed Machine Learning & Federated Learning, ML: Bayesian Learning, ML: Kernel MethodsAbstract
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.Downloads
Published
2024-03-24
How to Cite
Nawaz, A., Chen, G., Raza, M. U., Iqbal, Z., Li, J. ., Leung, V. C., & Chen, J. (2024). Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14431-14439. https://doi.org/10.1609/aaai.v38i13.29357
Issue
Section
AAAI Technical Track on Machine Learning IV