2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CR2021
Towards General-purpose Infrastructure for Protecting Scientific Data Under Study
Andrew Trask, Kritika Prakash
The scientific method presents a key challenge to privacy because it requires many samples to support a claim. When samples are commercially valuable or privacy-sensitive enough, t…
cs.LG2021★ 2 cited
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Alexander Ziller, Dmitrii Usynin, Moritz Knolle +6
In recent years, formal methods of privacy protection such as differential privacy (DP), capable of deployment to data-driven tasks such as machine learning (ML), have emerged. Rec…
cs.LG2021
Syft 0.5: A Platform for Universally Deployable Structured Transparency
Adam James Hall, Madhava Jay, Tudor Cebere +20
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. Th…