Date Published: September 17, 2025
Comments Due:
Email Questions to:
Planning Note (11/14/2025):
The public comment period has been extended through December 5, 2025.
Author(s)
Gary Howarth (NIST), Micah Altman (MIT), Sharon Ayalde (Harvard University), Elena Ghazi (Harvard University), Chuck McCallum (Harvard University), Priyanka Nanayakkara (Harvard University), Salil Vadhan (Harvard University), Rachel Cummings (Columbia University), Damien Desfontaines (Hiding Nemo), Jack Fitzsimons (Oblivious Software), Robert Pisarczyk (Oblivious Software), Andrew Gruen (Working Paper), James Honaker (Mozilla), Nitin Kohli (University of California, Berkeley), Joseph Near (University of Vermont)
Announcement
This draft proposes a NIST-hosted database of community-contributed descriptions of differential privacy (DP) deployments.
DP is a mathematical definition of privacy, and DP deployments are computer systems that add calibrated noise to data to protect the privacy of individuals. Since DP is being used at scale in many contexts, a database of implementation descriptions is an important step to establish guidelines and best practices for its application.
The quality of the NIST-hosted repository proposed in IR 8588 is maintained through a public working group. This draft describes the data schema, working group function, and other important considerations.
The public comment period is open through November 14, 2025 December 5, 2025. NIST invites the public to comment on the plan and especially welcomes contributions that will help maximize the value of the DP Registry. NIST encourages you to use this template when preparing your comments--thank you.
NOTE: A call for patent claims is included this draft. For additional information, see the Information Technology Laboratory (ITL) Patent Policy – Inclusion of Patents in ITL Publications.
There is a need in the community of privacy practitioners for a trustworthy, collaborative shared database of differentially private deployments, to help foster norms about best practices. We propose a shared governance resource to (1) help industry grow to consensus on best practices, (2) provide public snapshots of the privacy landscape so that regulators can judge new deployments in context and shape guidance accordingly, and (3) incentivize industry to make their choices public. We describe an initial schema to systematize this information and an editorial and governance process to ensure this information is reliable.
There is a need in the community of privacy practitioners for a trustworthy, collaborative shared database of differentially private deployments, to help foster norms about best practices. We propose a shared governance resource to (1) help industry grow to consensus on best practices, (2) provide...
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There is a need in the community of privacy practitioners for a trustworthy, collaborative shared database of differentially private deployments, to help foster norms about best practices. We propose a shared governance resource to (1) help industry grow to consensus on best practices, (2) provide public snapshots of the privacy landscape so that regulators can judge new deployments in context and shape guidance accordingly, and (3) incentivize industry to make their choices public. We describe an initial schema to systematize this information and an editorial and governance process to ensure this information is reliable.
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Keywords
privacy; differential privacy; privacy-enhancing technology; data governance
Control Families
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