Democratizing Health Research Through Privacy-Protecting Synthetic Data

This project aims to enable significantly broader use of health data by creating differentially private synthetic data sets. This project will also contribute to solutions for the focus on the coronavirus pandemic. It is supported by the UC Davis CeDAR.

Privacy-Preserving Data Analysis for Scientific Discovery

This project aims to produce methods, processes, and architectures applicable to a variety of scientific computing domains that enables querying, machine learning, and analysis of data while protecting against releasing sensitive information beyond pre-defined bounds. It is supported by LBNL CSR funds and is led by Sean Peisert.

Medical Science DMZ

We have defined a Medical Science DMZ as a method that allows data flows at scale while simultaneously addressing the HIPAA Security Rule and related regulations governing biomedical data and appropriately managing risk.

Secure and Private Acquisition, Storage, and Analysis of Medical Sensor Data

This project is developing a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure.