2 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework
Kritika Prakash, Fiza Husain, Praveen Paruchuri +1
Reinforcement Learning (RL) enables agents to learn how to perform various tasks from scratch. In domains like autonomous driving, recommendation systems, and more, optimal RL poli…
An automatic differentiation system for the age of differential privacy
Dmitrii Usynin, Alexander Ziller, Moritz Knolle +4
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML). Optimal noise calibration in this set…
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…
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…