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
"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
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 |
🚦 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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