NSF award 2453009 · Harvard University

PDaSP: Track 3: Rigorous and Performant Differentially Private Machine Learning via OpenDP

Final report due

28 January 2029

in 833 days · The award ends on 30 September 2028.

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Decide about an extension2 Jun 2028in 593 days
award ending 2028-09-30 · Research.gov
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Extension request16 Aug 2028in 668 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Sep 2028in 703 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 30 September 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 January 2029.

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Award card

Award number: 2453009
Period of support: 1 Oct 2025 – 30 Sep 2028
Amount: $800,000
Awardee: Harvard University, Cambridge, MA
Program: NSF-Intel Semiconductr Partnrs, Privacy Preserving Data Sharin
Directorate: TIP

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…

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