1 code implementation • 26 Apr 2021 • Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen Lennart van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, Chinmay Shah, Alan Aboudib, Théo Ryffel, Kritika Prakash, Tom Titcombe, Varun Kumar Khare, Maddie Shang, Ionesio Junior, Animesh Gupta, Jason Paumier, Nahua Kang, Vova Manannikov, Andrew Trask
We present Syft 0. 5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems.
no code implementations • 7 Dec 2018 • Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy.