Consortium forVerified Computing

O-0215

OpenMined

A nonprofit developing PySyft, software for joint analysis of private data, including confidential AI evaluations.

Source reviewed 2026-09-25Provider-reported evidence

01 / The mechanism and its boundary

What is being described

OpenMined is a nonprofit that develops open-source software for work across private datasets S-3564. Its work on this map is PySyft double-blind evaluations:

  • OpenMined reports that PySyft coordinated the submission, approval and execution of private code and assets in GPU enclaves for 2026 evaluations by AVERI and Singapore AISI S-3563.
  • The pilot's technical report describes a run of Gemini 2.5 Flash Lite on private MLCommons AILuminate prompts in a Google Cloud confidential VM with an H100, using PySyft v0.10.x S-3320.

Organization website ↗

Connections in the research map

Related research

mechanism / R2

Confidential multi-party verification

Lets mutually distrusting parties run an agreed check over private models or records inside attested enclaves or zero-knowledge proofs, revealing only the result.

implementation / R2

PySyft double-blind evaluations

PySyft coordinates an attested enclave where a model owner and evaluator run tests without sharing weights or private prompts.

Sources and provenance

  1. S-3564 / Tier A

    Announcement: CAISI signs CRADA with OpenMined to Enable Secure AI Evaluations ↗

    National Institute of Standards and Technology · 2026 · National Institute of Standards and Technology

    Supports: nonprofit status, open-source software for secure work across organizations, PySyft research agreement

    Locator: first paragraph

    Version and catalogue details
  2. S-3563 / Tier C

    PySyft used for first double-blind evaluation of a proprietary, frontier-class AI model ↗

    OpenMined Team · 2026 · OpenMined

    Supports: nonprofit status, PySyft development and role in double-blind evaluations

    Locator: Executive Summary; Details of the pilots; page footer

    Version and catalogue details
  3. S-3320 / Tier B

    Double Blind Evals: Resolving the Dual Confidentiality Dilemma in AI Safety Auditing ↗

    A. Trask, S. Messing, V. Pahwa, P. Maham, R. Kolga, A. Frantz, A. Tash, K. Thomas, S. McGregor, G. Balston, P. Paskov, M. Brundage, A. Vij, B. Hillenbrand, A. Karargyris, T. Acosta, J. Fenster, M. Eilish, R. Elasmar, M. Khan, K. van der Veen, R. S, S. Wagh, S. Gabriel, P. Werneck, L. Strahm, K. McDonough, R. Falcon, K. Lum, W. Isaac · 2026 · Google DeepMind

    Supports: PySyft in the 2026 pilot and OpenMined author affiliations

    Locator: abstract; §2.5; §3

    Version and catalogue details
Source review date
2026-09-25
Drafted by (source map)
ai
Review handles (source map)
codex-review