On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud

Dimitrios Melissourgos, Hanzhi Gao, Chaoyi Ma, Shigang Chen, Samuel S. Wu

Abstract: Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. However, the privacy concern prevents medical institutes from outsourcing the training process to the existing infrastructure on the cloud. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data holder to outsource privacy-sensitive medical data to the cloud in a masked form, and create ANN models that can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Parkinson’s disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.