Machine learning in precision medicine to preserve privacy via encryption

Precision medicine is an emerging approach for disease treatment and prevention that delivers person-alized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles. Despite the rapid advancement of precision medicine and its considerable promise, several underlying technological challenges remain unsolved. One such challenge of great importance is the se-curity and privacy of precision health-related data, such as genomic data and electronic health records, which stifle collaboration and hamper the full potential of machine-learning (ML) algorithms. To preserve data privacy while providing ML solutions, this article makes three contributions. First, we propose a generic machine learning with encryption (MLE) framework, which we used to build an ML model that predicts cancer from one of the most recent comprehensive genomics datasets in the field. Second, our framework's prediction accuracy is slightly higher than that of the most recent studies conducted on the same dataset, yet it maintains the privacy of the patients' genomic data. Third, to facilitate the validation, reproduction, and extension of this work, we provide an open-source repository that contains the design and implementation of the framework, all the ML experiments and code, and the final predictive model deployed to a free cloud service. (c) 2021 Elsevier B.V. All rights reserved.