Collaboration between Microsoft and Harvard’s Institute for Quantitative Social Science has developed an open data differential privacy platform and continues to open new research.Learn about our partnership
How do we create machine learning models that preserve the privacy of individuals while including the broadest possible data? Patients can opt out of sharing data, but that may exacerbate imbalanced or inaccurate datasets. Anonymizing data may eliminate elements critical to answering research questions. Researchers need a way to include all the data available, while still protecting the anonymity of individuals.
Differential privacy simultaneously enables researchers and analysts to extract useful insights from datasets containing personal information and offers stronger privacy protections. This is achieved by introducing “statistical noise”. The noise is significant enough to protect the privacy of any individual, but small enough that it will not impact the accuracy of the answers extracted by analysts and researchers.
We are working with our partners to build open toolkits to better enable differential privacy. Further combined with other security services like Confidential Compute – differential privacy can help researchers find answers to their questions, while protecting the privacy of the individual.
Accelerating health innovation with differential privacy
Our partnership with the Cascadia Data Discovery Initiative (CDDI) aims to tackle the barriers that make breakthroughs in research difficult, starting with barriers to data discovery and data access.
- Learn about machine learning on Azure
- Learn about responsible AI
- Learn about Azure Confidential Compute
- New Cascadia Data Discovery Initiative accelerates health innovation
- Learn about AI, privacy, and encryption at the Microsoft Research podcast
- Review publications on differential privacy from Microsoft Research
- Learn about collecting telemetry data privately
JFK Files takes 34,000 complex files including photos, handwriting, government documents, and more, then extracts readable information. This knowledge is organized to enable new ways to explore the information.
Responsible Conversational AI
Conversational AI is a new way for companies to interact with their customers across any channel, like digital assistants, chat or social media. To be effective, conversational bots need to be developed in a way that earns people’s trust.
Homomorphic Encryption (HE)
HE technology allows computations to be performed directly on encrypted data. Using state-of-the-art cryptology, you can run machine learning on anonymized datasets without losing context.
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