Project Laplace

Project Laplace

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 ad…

Migrants rescued last March in the Channel of Sicily by Italian Coast Guard (File photo). © Francesco Malavolta/IOM 2015
微软研究院博客

Microsoft is home to a diverse team of r…

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 …

新闻报道 | VentureBeat

Microsoft announced the addition of new …

新闻报道 | Microsoft Open Source Blog

The code for WhiteNoise, the first versi…

微软研究院博客

| Bolin Ding, Janardhan (Jana) Kulkarni, 和 Sergey Yekhanin

The collection and analysis of telemetry…