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Found 6 papers, 3 papers with code
Syft 0.5: A Platform for Universally Deployable Structured Transparency
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.
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Reconciling large-scale ML with the closed-form reasoning required for the principled analysis of individual privacy loss requires the introduction of new tools for automatic sensitivity analysis and for tracking an individual's data and their features through the flow of computation.
An automatic differentiation system for the age of differential privacy
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML).
Towards General-purpose Infrastructure for Protecting Scientific Data Under Study
The scientific method presents a key challenge to privacy because it requires many samples to support a claim.
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework
Reinforcement Learning (RL) enables agents to learn how to perform various tasks from scratch.
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake.
