🛡️ 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...).

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🎯 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.

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🛠️ Example

"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."
}

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📚 Fields

Field	Purpose
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

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🚦 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.

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⚠️ Limitations

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

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📡 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

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🚀 Status

Experimental / Conceptual Proposal

Designed to spark discussion and explore integration patterns.

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