Applying Advanced Data Privacy Methods to Real World Data (RWD)

Project Description 

This project aims to develop a comprehensive, cross-disciplinary resource to support the application of advanced privacy-pressing techniques to real-world data (RWD). It addresses the growing complexity of integrating diverse data sources and the increasing use of machine learning (ML), artificial intelligence (AI) and large language models (LLMs) within healthcare. 

RWD presents significant privacy risks due to its sensitivity, high dimensionality, and potential for re-identification. While methods such as differential privacy, federated learning and synthetic data generation exist, there is limited consolidated guidance on their practical implementation, particularly in AI-driven contexts. 

The project will provide actionable guidance on applying these techniques in compliant, scalable and scientifically robust ways. It will leverage open-source datasets (e.g. MIMIC-IV, UK Biobank, ADNI) for validation and demonstration. The resulting resource will support researchers, developers and regulators in advancing privacy-preserving data integration and responsible AI adoption. 

Abhishek Mishra, GSK

abhishek.2.mishra@gsk.com

Elena Valkanova, Independent

evalkanova75@yahoo.com

Nicola Newton, PHUSE Project Coordinator

nicky@phuse.global