ACCESS CONTROL FOR PRIVACY-PRESERVING GAUSSIAN PROCESS REGRESSION

In this paper, we propose access control for privacy-preserving Gaussian process regression (GPR), in which the encrypted data are generated through a random unitary transform (RUT). The proposed secure GPR enables computation in both encrypted input and output domains, and the access to inputs and prediction results can be controlled. We prove that our GPR for encrypted data has the same prediction accuracy as GPR for non-encrypted data. Furthermore, we demonstrate the effectiveness of our method by experimenting with diabetes data from the medical analysis field.