Differential privacy
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A little public data makes privacy-preserving AI models more accurate
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Many useful computer vision models are trained on large corpora of public data, such as ImageNet. But some applications — models that analyze medical images ...

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Better differential privacy for end-to-end speech recognition
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Modern AI models, such as those that recognize images and speech, are highly data dependent. While some public-domain data sets are available to train such ...

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Differential privacy for deep learning at GPT scale
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Deep-learning models are data driven, and that data may contain sensitive information that requires privacy protection. Differential privacy (DP) is a ...

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Computing on private data
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Many of today’s most innovative computation-based products and solutions are fueled by data. Where those data are private, it is essential to protect them ...

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A quick guide to Amazon’s papers at ICML
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At this year's International Conference on Machine Learning (ICML), Amazon researchers have several papers on bandit problems and differential privacy, two ...

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