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Privacy-preserving generative deep neural networks support clinical data sharing

View ORCID ProfileBrett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/159756
Brett K. Beaulieu-Jones
1Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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  • ORCID record for Brett K. Beaulieu-Jones
Zhiwei Steven Wu
2Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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Chris Williams
3Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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Casey S. Greene
3Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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  • For correspondence: csgreene@upenn.edu
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Abstract

Though it is widely recognized that data sharing enables faster scientific progress, the sensible need to protect participant privacy hampers this practice in medicine. We train deep neural networks that generate synthetic subjects closely resembling study participants. Using the SPRINT trial as an example, we show that machine-learning models built from simulated participants generalize to the original dataset. We incorporate differential privacy, which offers strong guarantees on the likelihood that a subject could be identified as a member of the trial. Investigators who have compiled a dataset can use our method to provide a freely accessible public version that enables other scientists to perform discovery-oriented analyses. Generated data can be released alongside analytical code to enable fully reproducible workflows, even when privacy is a concern. By addressing data sharing challenges, deep neural networks can facilitate the rigorous and reproducible investigation of clinical datasets.

One Sentence Summary Deep neural networks can generate shareable biomedical data to allow reanalysis while preserving the privacy of study participants.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 05, 2017.
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Casey S. Greene
bioRxiv 159756; doi: https://doi.org/10.1101/159756
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Casey S. Greene
bioRxiv 159756; doi: https://doi.org/10.1101/159756

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