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.

Provable Anonymization of Grid Data for Cyberattack Detection

This project aims to develop techniques for enabling data analysis for the purposes of detecting and/or investigating cyberattacks against energy delivery systems while also preserving aspects of key confidentiality elements within the underlying raw data being analyzed. The result will be a complete solution for anonymization of data collected from OT and IT networks pertaining to energy grid cyberattack detection that has been tested for its ability to retain privacy properties and still enable attack detection. It is funded by DOE CESER’s CEDS program and is led by Sean Peisert.

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.

I3P Data Sanitization

This project looked at defining means for understanding what data can be sanitized, and how. At LBNL, this project was led by Sean Peisert and was funded by the Institute for Information Infrastructure Protection (I3P).