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PySyft protects data privacy. What about AI Agent identity? #9401

@Marsssssssssssdsss

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@Marsssssssssssdsss

PySyft is one of the most important projects in privacy-preserving AI. The work on federated learning, secure MPC, and differential privacy is foundational.

I want to raise a related concern that I don't see discussed enough in the privacy AI community:

PySyft protects the DATA during computation.
But who is running the computation?

In a federated learning setup:

  • Multiple parties contribute data to train a model
  • The data is protected through encryption and secure protocols
  • But what verifies the identity of the participants?
  • What prevents a malicious actor from joining the federation under a false identity?

This is an identity problem, not a data protection problem. And it becomes critical as AI agents become more autonomous:

  • AI agents participating in federated learning
  • AI agents making decisions based on verified data
  • AI agents representing organizations in multi-agent systems

We're building an open-source AI identity verification framework: https://github.com/Marsssssssssssdsss/nexus6

The idea is simple: every AI agent should have a verifiable identity that can be checked before it participates in any sensitive computation or data exchange.

I'd love to hear from the OpenMined community:

  1. Have you encountered identity spoofing issues in federated learning deployments?
  2. Do you think AI Agent identity verification should be part of the privacy-preserving AI stack?
  3. Would a verification layer be useful for PySyft deployments?

The OpenMined community has been at the forefront of thinking about AI ethics and privacy. Your perspective on AI identity would be invaluable.

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