Privacy-Preserving Publishing of Individual-Level Medical Data for Cloud Services

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Abstract
Deep learning (DL) has been extensively adopted in many applications, including disease prediction. Most DL-based applications are executed on a cloud server because the DL models are too large and complicated to be executed on the client-side. De facto cloud-hosted inferences lead to privacy concerns regarding services that operate on personal medical data. Nevertheless, given the recent development of DL-based applications for health-diagnosis services, these applications have become a dominant means of healthcare support in our daily lives. To prevent the misuse of personal medical data, several techniques have been developed to preserve sensitive information, with a trade-off between privacy and utility. A simple method that offers privacy preservation and good prediction performance involves the deployment of a diagnostic method to the client side. However, doing so makes DL models more vulnerable to adversaries. To this end, we propose a deep private generative framework that guarantees user-data privacy while maintaining the original class information and protecting the models from reverse engineering. Experimentation with practical deep neural networks on benchmark disease datasets demonstrates that the proposed method decreases the mutual information between the original data and synthetic data by nearly 80% while preserving a prediction accuracy of nearly 95% of the original prediction accuracy.

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