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 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.

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 researchers focused on supporting a healthy global society, including finding ways technology can address human rights problems affecting the most vulnerable populations around the world. With a multi-disciplinary background in human-computer interaction, data…

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…

新闻报道 | VentureBeat

Microsoft announced the addition of new capabilities to Azure Machine Learning, its cloud-based environment for training, deploying, and managing AI models. WhiteNoise, a toolkit for differential privacy, is now available both through Azure and in open source on GitHub, joining…

新闻报道 | Microsoft Open Source Blog

The code for WhiteNoise, the first version of the open source differential privacy platform, is now live on GitHub. The project is jointly developed by Microsoft and Harvard’s Institute for Quantitative Social Science (IQSS) and the School of Engineering and…

微软研究院博客

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

The collection and analysis of telemetry data from users and their devices leads to improved user experiences and informed business decisions. However, users have concerns about their data privacy, including what personal information software and internet companies are gathering and…