Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing.

Pubmed ID: 31284738

Pubmed Central ID: PMC7041894

Journal: Circulation. Cardiovascular quality and outcomes

Publication Date: July 1, 2019

MeSH Terms: Humans, Data Collection, Hypertension, Randomized Controlled Trials as Topic, Treatment Outcome, Blood Pressure, Computer Simulation, Antihypertensive Agents, Information Dissemination, Computer Security, Confidentiality, Deep Learning

Grants: K23 HL128909

Authors: Wu ZS, Lee R, Beaulieu-Jones BK, Williams C, Bhavnani SP, Byrd JB, Greene CS

Cite As: Beaulieu-Jones BK, Wu ZS, Williams C, Lee R, Bhavnani SP, Byrd JB, Greene CS. Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing. Circ Cardiovasc Qual Outcomes 2019 Jul;12(7):e005122. Epub 2019 Jul 9.

Studies:

Abstract

BACKGROUND: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. METHODS AND RESULTS: Using pairs of deep neural networks, we generated simulated, synthetic participants that closely resemble participants of the SPRINT trial (Systolic Blood Pressure Trial). We showed that such paired networks can be trained with differential privacy, a formal privacy framework that limits the likelihood that queries of the synthetic participants' data could identify a real a participant in the trial. Machine learning predictors built on the synthetic population generalize to the original data set. This finding suggests that the synthetic data can be shared with others, enabling them to perform hypothesis-generating analyses as though they had the original trial data. CONCLUSIONS: Deep neural networks that generate synthetic participants facilitate secondary analyses and reproducible investigation of clinical data sets by enhancing data sharing while preserving participant privacy.