The Swiss Blockchain Winter School

The Swiss Blockchain Winter School is a follow-up event to the Swiss Blockchain Summer School 2017 and an IACR Cryptology School. The goal of the event is to bring together students, academic researchers, industry professionals and government employees for an in-depth exchange on the latest trends and developments in cryptocurrencies, distributed ledger technologies, decentralized applications, and decentralizing trust. Distributed ledger technologies and decentralized trust systems are integral to achieving the goals of DPPH/PHRT of building an accountable and privacy-preserving data-sharing platform without relying on centralized entities.

Talk at the 2nd Annual Delft Blockchain Lab Symposium

Presenter: Bryan Ford, Title: “Coins, Clubs, and Crowds: Scaling and Decentralization in Next-Generation Blockchains and Cryptocurrencies.” [slides] Venue: Technische Universiteit Delft, Delft, Netherlands, 31 January 2019 link

Distinguished lecture at the University of Luxembourg

Presenter: Bryan Ford, Title: “Coins, Clubs, and Crowds: Scaling and Decentralization in Next-Generation Blockchains and Cryptocurrencies.” [slides] Venue: University of Luxembourg, Luxembourg, 5 December 2018 link

Talk in the framework of the ECOS project funded by the Leenaards Foundation

The goal of the ECOS project is to bring together scientists and citizens in order to discuss around the challenges and open questions related to personalized medicine. Researchers from the DPPH project participated in a workshop to discuss about the privacy concerns related to medical data sharing. A presentation of GenoShare was delivered during the workshop.

Talk at the ITU-WHO workshop on Artificial Intelligence for Health

This was the second workshop organized by ITU and WHO about standardized assessment of AI for health. We presented the DPPH project and a potential approach based on advanced privacy-enhancing technologies that can address the privacy and security concerns related to benchmarking AI models on sensitive medical data.