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[…] the companies are permitted to share any de- identified data. If the tracking data is shared according to rules for de-identified data, then regardless of what is promised in principle, in practice taking a name off a dataset accomplishes very little. The second class of threats is broadly concerned with an improved ability to […]

In October 2020, the Future of Privacy Forum (FPF) convened a virtual workshop entitled “Privacy and Pandemics: Responsible Uses of Technology and Health Data During Times of Crisis” with invited computer science, privacy law, public policy, social science, and health information experts from around the world to examine benefits, risks, and strategies for the collection […]

On Wednesday, December 9, 2020, FPF hosted a Digital Data Flows Masterclass on Machine Learning and Speech. The masterclass on Machine Learning and Speech is the first masterclass of a new series after completing the VUB-FPF Digital Data Flows Masterclass series with eight topics. Professor Marine Carpuat (Associate Professor in Computer Science at the University of Maryland) starts […]

COVID-19: FEDERATED LEARNING FOR PRIVACY PRESERVING MULTI-INSTITUTIONAL COLLABORATION

[…] apps: centralized apps where the data are stored by a central authority and decentralized apps that send and store data in a distributed manner. If implemented as promised, a decentralized app (most use the Google/Apple-supported privacy-respecting approach and API 3 ) can prevent, or at least limit, the abuse of data. But this approach […]

[…] apps: centralized apps where the data are stored by a central authority and decentralized apps that send and store data in a distributed manner. If implemented as promised, a decentralized app (most use the Google/Apple-supported privacy-respecting approach and API 3 ) can prevent, or at least limit, the abuse of data. But this approach […]