[P3] Sprint 3.3: Integrate Concrete-ML for F9 Cryptographic Proofs #176
Description
Objective
Replace conceptual zk-SNARKs with real FHE proofs for F9 Anti-Hantu.
Repository
https://github.com/zama-ai/concrete-ml
Effort
6 days
Timeline
Future (v57.0+) — after market validation
Implementation
AuthenticityProver Class
from concrete.ml.sklearn import LogisticRegression
class AuthenticityProver:
def __init__(self):
self.model = LogisticRegression(n_bits=4)
async def prove_authenticity(self, text: str) -> dict:
"""Generate FHE proof of non-deception"""
features = self.extract_features(text)
proof = self.model.predict_proba(features, fhe="execute")
return {
"authentic": proof[1] > 0.70,
"fhe_proof": proof.serialize()
}Apex Integration
@mcp.tool()
async def apex_verdict(query: str, session_id: str) -> dict:
prover = AuthenticityProver()
proof = await prover.prove_authenticity(query)
if not proof["authentic"]:
return {"verdict": "VOID", "reason": "F9 violation"}
# ... rest of logicSubtasks
- Install Concrete-ML:
pip install concrete-ml - Create
codebase/floors/f9_authenticity.py - Train model on deception dataset
- Integrate with
apex_verdict - Test FHE proof generation (~10s)
- Document FHE limitations
Acceptance Criteria
- F9 uses real FHE proofs (not conceptual)
- Authenticity score ≥ 0.70 required for SEAL
- Proof verifiable without revealing input
Blockers
None (can run in parallel)
Priority
P3 — Evaluate after v56.0 market validation
Files Changed
codebase/floors/f9_authenticity.py(replace stub)codebase/mcp/tools/apex_tool.pytests/test_f9_fhe.py(new)
Related
- INTEGRATION_MASTERPLAN.md