360 citations · 449 across the 19 of their papers we have counts for
19 papers
Risk of re-identification for shared clinical speech recordings
Daniela A. Wiepert, Bradley A. Malin, Joseph R. Duffy +4
Large, curated datasets are required to leverage speech-based tools in healthcare. These are costly to produce, resulting in increased interest in data sharing. As speech can poten…
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report
Chris Clifton, Bradley Malin, Anna Oganian +2
Government agencies collect and manage a wide range of ever-growing datasets. While such data has the potential to support research and evidence-based policy making, there are conc…
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Prithwish Chakraborty, James Codella, Piyush Madan +22
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability…
Re-identification of Individuals in Genomic Datasets Using Public Face Images
Rajagopal Venkatesaramani, Bradley A. Malin, Yevgeniy Vorobeychik
DNA sequencing is becoming increasingly commonplace, both in medical and direct-to-consumer settings. To promote discovery, collected genomic data is often de-identified and shared…
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Siddharth Biswal, Soumya Ghosh, Jon Duke +3
Researchers require timely access to real-world longitudinal electronic health records (EHR) to develop, test, validate, and implement machine learning solutions that improve the q…
GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment
Aref Asvadishirehjini, Murat Kantarcioglu, Bradley Malin
Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide range of applications ranging from self-driving cars to COVID-19 treatment discover…