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新闻与深度文章

A flow chart with four successive blocks. Starting with a data owner, private data is provisioned to train a language model with differential privacy. The language model is subsequently prompted to generate novel synthetic data resembling the private data. This data can be used for down-stream applications such as machine learning, feedback analysis or statistical analysis.
微软研究院博客

| Gbola Afonja, Robert Sim, Zinan Lin, Huseyin Atahan Inan, 和 Sergey Yekhanin

Synthetic data could potentially help address some privacy concerns with AI model development and training, but it comes with limitations. Researchers at Microsoft are exploring techniques for producing more realistic data with strong privacy protections.

Graphic shows framework of Privacy Preserving Machine Learning
微软研究院博客

| Victor Ruehle, Robert Sim, Sergey Yekhanin, Nishanth Chandran, Melissa Chase, Daniel Jones, Kim Laine, Boris Köpf, Jaime Teevan, Jim Kleewein, 和 Saravan Rajmohan

Machine learning (ML) offers tremendous opportunities to increase productivity. However, ML systems are only as good as the quality of the data that informs the training of ML models. And training ML models requires a significant amount of data, more…