Project Laplace
News & features
The Crossroads of Innovation and Privacy: Private Synthetic Data for Generative AI
| Gbola Afonja, Robert Sim, Zinan Lin, Huseyin Atahan Inan, and 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.
IOM and Microsoft release first-ever differentially private synthetic dataset to counter human trafficking
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…
Privacy Preserving Machine Learning: Maintaining confidentiality and preserving trust
| Victor Ruehle, Robert Sim, Sergey Yekhanin, Nishanth Chandran, Melissa Chase, Daniel Jones, Kim Laine, Boris Köpf, Jaime Teevan, Jim Kleewein, and 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…
In the news | VentureBeat
Microsoft debuts WhiteNoise, an AI toolkit for differential privacy
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…
In the news | Microsoft Open Source Blog
Introducing WhiteNoise: the new differential privacy platform from Microsoft and Harvard’s OpenDP
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…
Collecting telemetry data privately
| Bolin Ding, Janardhan (Jana) Kulkarni, and 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…