Security and Privacy for Distributed Learning Techniques in IoT-Driven Health Services (SePRIO)

The surge in digital health data is a significant phenomenon, leading to numerous technical and practical challenges regarding how these data will be shared, processed, and stored in efficient, secure, privacy-preserving, and scalable solutions for various use cases in the health domain. Therefore, there is a strong need for innovative, robust, and privacy-preserving distributed learning techniques that effectively implement the underlying data processing/analytics principles without compromising efficiency and utility in health applications. Specifically, secure aggregation and privacy-preserving techniques for distributed learning, such as Federated Learning (FL), should be implemented, tested, and integrated in health use cases with IoT devices.

In that regard, the primary objective of the SePRIO project is to investigate and integrate security and privacy into distributed learning techniques, with a focus on digital health use cases involving IoT. To this end, first, we will explore a wide range of secure aggregation and privacy-preserving techniques. Then, we will integrate these techniques into our AI infrastructure and use our augmented distributed learning models (e.g., FL) to analyze open health dataset(s). Finally, we will explore the limitations and trade-offs.

This project aims for various outcomes, including an open-source software asset, related publications, talks and presentations, and concrete collaboration between two EELISA universities, ZHAW and the Istanbul Technical University ITU.