PDaSP: Track 3: Rigorous and Performant Differentially Private Machine Learning via OpenDP — NSF Award to Harvard University (MA,
Artificial intelligence systems are increasingly trained using datasets containing private information about individuals in critical areas such as government services, healthcare, and education. However, these AI systems have a demonstrated risk of accidentally revealing sensitive personal information about the people
| Award title | PDaSP: Track 3: Rigorous and Performant Differentially Private Machine Learning via OpenDP |
|---|---|
| Award ID | 2453009 |
| Awardee | Harvard University |
| City | CAMBRIDGE |
| State | MA |
| Amount obligated | $800,000 |
| Principal investigator | Salil Vadhan |
| Program | NSF-Intel Semiconductr Partnrs, Privacy Preserving Data Sharin |
| Start date | 10/01/2025 |
| Abstract | Artificial intelligence systems are increasingly trained using datasets containing private information about individuals in critical areas such as government services, healthcare, and education. However, these AI systems have a demonstrated risk of accidentally revealing sensitive personal information about the people whose data was used during training, creating serious privacy and security concerns. This problem threatens public trust in AI technologies and creates barriers to beneficial uses |
| Source | NSF Awards |
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