3 research outputs found
Privacy-preserving collaborative machine learning on genomic data using TensorFlow
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.
We design and implement several MPC-friendly ML primitives, including class
weight adjustment and parallelizable approximation of activation function. In
addition, we develop the solution as an extension to TF
Encrypted~\citep{dahl2018private}, enabling us to quickly experiment with
enhancements of both machine learning techniques and cryptographic protocols
while leveraging the advantages of TensorFlow's optimizations. Our
implementation compares favorably with state-of-the-art methods, winning first
place in Track IV of the iDASH2019 secure genome analysis competition.Comment: Description of the winning solution at Track IV of iDASH competition
2019, to be presented at the Trustworthy ML workshop co-located with ICLR202