LLMFeed Extension — Homomorphic Encryption

🛡️ LLMFeed Extension — Homomorphic Encryption

This is a hypothetical extension exploring how homomorphic encryption could enhance .llmfeed.json feeds as secure capsules for multi-agent processing.

A signed .llmfeed.json is already a verifiable capsule — ensuring the integrity of both payload and context.

Homomorphic encryption would allow agents to manipulate certain encrypted fields within that capsule:

✅ without breaking signature integrity
✅ without exposing raw data
✅ while enabling pipeline processing across agents (healthcare, finance, legal, administration...).


🎯 Purpose

  • Treat .llmfeed.json feeds as secure processing capsules.
  • Enable privacy-preserving pipelines between agents.
  • Allow computation on encrypted data without breaking trust boundaries.
  • Maintain the integrity of signed feeds even as agents process the encrypted parts.

🛠️ Example

json
"homomorphic_encryption": {
  "applied_to": ["data"],
  "algorithm": "BFV",
  "public_parameters": "https://example.com/params.json",
  "notes": "Data is homomorphically encrypted to allow LLM-safe processing without exposing raw data."
}

📚 Fields

FieldPurpose
`applied_to`List of blocks the encryption applies to (e.g., `["data"]`)
`algorithm`Encryption algorithm (e.g., `BFV`, `CKKS`, `Paillier`, etc.)
`public_parameters`URL to fetch encryption parameters needed for processing
`notes`Optional human-readable notes

🚦 Agent Behaviour

Agents MAY:

✅ Recognize the presence of homomorphic_encryption.
✅ Adjust their reasoning capabilities accordingly.
✅ Skip actions requiring access to raw data unless decryption is possible.
✅ Indicate in UI that data is homomorphically protected.
✅ Preserve the integrity of signed blocks while processing encrypted fields.


⚠️ Limitations

  • Not yet a formal part of the LLMFeed standard.
  • Dependent on agent capabilities and cryptographic libraries.
  • Intended as a forward-looking, experimental extension.

📡 Summary

Homomorphic encryption can turn signed .llmfeed.json feeds into trusted capsules for multi-agent workflows:

Data remains encrypted → privacy preserved
Signatures remain valid → trust preserved
Processing is enabled → agents can compute on encrypted fields

This approach could enable privacy-preserving agent pipelines in sensitive domains:

  • Healthcare
  • Finance
  • Administration
  • Legal processes

🚀 Status

Experimental / Conceptual Proposal

Designed to spark discussion and explore integration patterns.


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