Privacy-preserving Bayesian Inference for Deep Gaussian Processes
Takayuki Nakachi, Yitu Wang · 2024
In this paper, we propose a privacy-preserving Bayesian inference method for deep Gaussian processes (DGPs). By hierarchically combining Gaussian processes (GPs) in DGPs, a more expressive model can be constructed. The proposed method enables Bayesian inference for DGPs while keeping the input data scrambled through a random unitary transform (RUT). Our Bayesian inference on scrambled data performs equivalently to Bayesian inference on non-scrambled data. Furthermore, we propose an access control method for prediction by changing the private keys of the RUT for the training and new inputs. Finally, we demonstrate the effectiveness of our method through experiments with diabetes data from the medical analysis field.