Privacy-preserving Gaussian Process Latent Variable Model for Dimensional Reduction
Takayuki Nakachi, Yitu Wang · 2023
In this paper, we propose a privacy-preserving Gaussian Process Latent Variable Model (GPLVM) for scrambled data generated on the basis of a random unitary transform. The GPLVM is a flexible Bayesian non-parametric modeling method that has been extensively studied and applied in many machine learning tasks. The proposed privacy-preserving GPLVM reduces the dimension of high-dimensional data in the scrambled domain in consideration of its use in the edge/cloud. In addition, we propose a dimensional extension method to improve security strength. We prove, theoretically, that the proposal has exactly the same estimation performance as the GPLVM for non-scrambled data. Finally, we perform numerical demonstrations on multiphase oil flow data. The effectiveness of the proposed method is verified.