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Found 18 papers, 7 papers with code
Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases
The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis.
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.
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).
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.
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?
Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, which leads to a significantly high drop-in model utility for sub-populations that are under-represented (minorities), compared to well-represented ones.
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.
Beyond Privacy Trade-offs with Structured Transparency
Many socially valuable activities depend on sensitive information, such as medical research, public health policies, political coordination, and personalized digital services.
Federated Learning
Cryptography and Security
Computers and Society
Privacy-preserving medical image analysis
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains.
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute.
Benchmarking Differentially Private Residual Networks for Medical Imagery
In this paper we measure the effectiveness of $\epsilon$-Differential Privacy (DP) when applied to medical imaging.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
The Future of Digital Health with Federated Learning
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.
Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation.
A generic framework for privacy preserving deep learning
We detail a new framework for privacy preserving deep learning and discuss its assets.
Sample Efficient Adaptive Text-to-Speech
Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
Neural Arithmetic Logic Units
Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training.
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings
Neural word representations have proven useful in Natural Language Processing (NLP) tasks due to their ability to efficiently model complex semantic and syntactic word relationships.
Modeling Order in Neural Word Embeddings at Scale
Natural Language Processing (NLP) systems commonly leverage bag-of-words co-occurrence techniques to capture semantic and syntactic word relationships.
