PARAPET, a research platform for using data while protecting it — NSF AI grant $5M (San Diego State, NAIRR)
NSF awarded about $5 million to "PARAPET: Prototype Architecture for Research Advances using Privacy Enhancing Technologies" at the San Diego State University Foundation. Made under the National AI Research Resource (NAIRR), it aims to prototype a platform that uses privacy-preserving technology to make research data usable.
Grant overview (primary data)
- Award amount$4,999,963
- RecipientSan Diego State University Foundation (California)
- ProgramNAIRR-Nat AI Research Resource
- Period2026-09-15 〜 2031-08-31
- FunderU.S. National Science Foundation (NSF) / NSF
Key points
- NSF Award 2537035, "PARAPET: Prototype Architecture for Research Advances using Privacy Enhancing Technologies," to the San Diego State University Foundation (CA).
- Program: NAIRR (National AI Research Resource).
- The subject is a prototype research platform built on privacy enhancing technologies (PETs).
- Obligated $4,999,963, equal to the estimated total. Period: September 15, 2026 to August 31, 2031.
- No abstract is included in this dataset; see the official NSF page for detail.
- Privacy-enhancing technology names several methods: computing while encrypted, learning without pooling, and adding noise to statistics.
1What privacy enhancing technologies are
Privacy Enhancing Technologies (PETs) is the umbrella term for methods that allow analysis and computation without exposing the contents of the data. They include computing on encrypted data, learning in separate places and combining only the results rather than pooling raw records, and adding deliberate noise to statistics so individuals cannot be identified. What they share is offering an option other than "show everything in order to use it."
2Privacy as research infrastructure
AI research runs into a contradiction: the data with the highest value — health, education, government — is the data hardest to share as is. The phrase "Prototype Architecture" in the name indicates an intent to build a working mechanism that makes such data usable for research. Its placement within NAIRR also suggests this is aimed at shared infrastructure rather than a single research project.
3A five-year term
The period runs five years, September 2026 to August 2031, and the obligated amount equals the estimated total ($4,999,963). The specific plan is not in this dataset, which carries no abstract, so consult the official NSF page (Award 2537035).
4Options other than showing everything
What is called privacy-enhancing technology is not one method but a name for several with different aims.
- 1Compute while encryptedCarry out the operation without decrypting the contents
- 2Learn without poolingTrain in each place and gather only the results
- 3Add noise to statisticsPublish only in a form from which no individual can be identified
Data carrying sensitive information — health, education, government — is the most valuable for research and the hardest to share as it stands. Against that contradiction, this family of methods sets out not a choice between using and protecting but a range of options in between.
Why it matters
For businesses handling sensitive data, "protect while using" is displacing "collect first, decide later" as the design premise. Privacy enhancing technologies being built into national research infrastructure is a signal worth reading for future technical standards and procurement requirements.
FAQ
What are privacy enhancing technologies?
Why does research infrastructure need privacy technology?
Where can I read what the research covers?
Sources (primary)
Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.
- NSF Award (original, official)
- NSF Award ID: 2537035