Privacy-preserving collaborative machine learning on genomic data using TensorFlow
Cheng Hong, Zhicong Huang, Wenjie Lu, Hunter Qu, Li Ma, Morten Dahl, Jason Mancuso · Proceedings of the ACM Turing Celebration Conference - China · 2020
Machine learning (ML) methods have been widely used in genomic studies. However, genomic data are often held by different stakeholders (e.g. hospitals, universities, and healthcare companies) who consider the data as sensitive information, even though they desire to collaborate. To address this issue, recent works have proposed solutions using Secure Multi-party Computation (MPC), which train on the decentralized data in a way that the participants could learn nothing from each other beyond the final trained model.