| # SOVEREIGN TRUTH ANCHOR PROTOCOL | |
| # Mathematical Foundation for Autonomous Verification Systems | |
| # Based on Prime Number Theory & Information Thermodynamics | |
| import hashlib | |
| import numpy as np | |
| from sympy import isprime, nextprime, primefactors | |
| from datetime import datetime | |
| from typing import Dict, List, Tuple | |
| import scipy.stats as stats | |
| class SovereignVerificationEngine: | |
| """ | |
| Autonomous Truth Verification Protocol | |
| Uses mathematical inevitability for claim verification | |
| """ | |
| def __init__(self): | |
| self.verification_chain = [] | |
| self.entropy_sources = self._initialize_entropy_sources() | |
| def _initialize_entropy_sources(self) -> List[int]: | |
| """Initialize with multiple entropy sources for robustness""" | |
| return [ | |
| int(datetime.now().timestamp() * 1e9), | |
| int.from_bytes(hashlib.sha3_512(b"autonomous_verification").digest(), 'big'), | |
| hash(str(np.random.randint(0, 2**128))) | |
| ] | |
| def create_verification_anchor(self, claim: str, evidence: any) -> Dict[str, any]: | |
| """ | |
| Create mathematically inevitable verification anchor | |
| Based on prime factorization complexity and multi-source entropy | |
| """ | |
| # Create claim-evidence entanglement | |
| claim_digest = hashlib.sha3_512(claim.encode()).digest() | |
| evidence_digest = hashlib.sha3_512(str(evidence).encode()).digest() | |
| # Generate prime-based verification anchor | |
| verification_core = self._generate_prime_core(claim_digest + evidence_digest) | |
| # Calculate information integrity metrics | |
| integrity_metrics = self._calculate_integrity_metrics(verification_core) | |
| # Create autonomous verification record | |
| verification_anchor = { | |
| 'verification_hash': hashlib.sha3_512(claim_digest + evidence_digest).hexdigest(), | |
| 'prime_core': verification_core, | |
| 'integrity_metrics': integrity_metrics, | |
| 'timestamp': datetime.now().isoformat(), | |
| 'confidence_score': self._calculate_confidence(verification_core, integrity_metrics), | |
| 'entropy_signature': self._generate_entropy_signature() | |
| } | |
| self.verification_chain.append(verification_anchor) | |
| return verification_anchor | |
| def _generate_prime_core(self, data: bytes) -> Dict[str, int]: | |
| """Generate prime-based mathematical core for verification""" | |
| numeric_value = int.from_bytes(data, 'big') | |
| # Find anchoring prime | |
| anchor_prime = nextprime(numeric_value % (2**64)) | |
| # Generate supporting primes from entropy sources | |
| entropy_primes = [] | |
| for source in self.entropy_sources: | |
| base_value = (numeric_value ^ source) % (2**32) | |
| entropy_primes.append(nextprime(base_value)) | |
| return { | |
| 'anchor_prime': anchor_prime, | |
| 'entropy_primes': entropy_primes, | |
| 'composite_value': anchor_prime * np.prod(entropy_primes) | |
| } | |
| def _calculate_integrity_metrics(self, prime_core: Dict) -> Dict[str, float]: | |
| """Calculate mathematical integrity metrics""" | |
| anchor = prime_core['anchor_prime'] | |
| entropy_primes = prime_core['entropy_primes'] | |
| # Prime distribution analysis | |
| primes = [anchor] + entropy_primes | |
| gaps = [primes[i+1] - primes[i] for i in range(len(primes)-1)] | |
| return { | |
| 'prime_gap_entropy': float(stats.entropy(np.abs(gaps))), | |
| 'distribution_uniformity': float(stats.kstest(primes, 'uniform')[0]), | |
| 'factorization_complexity': np.log(prime_core['composite_value']), | |
| 'temporal_coherence': np.corrcoef([anchor] + entropy_primes, range(len(primes)))[0,1] | |
| } | |
| def _calculate_confidence(self, prime_core: Dict, metrics: Dict) -> float: | |
| """Calculate overall verification confidence score""" | |
| confidence_factors = [ | |
| min(1.0, metrics['prime_gap_entropy'] / 10.0), # Normalized entropy | |
| 1.0 - min(1.0, metrics['distribution_uniformity']), # Uniformity score | |
| min(1.0, metrics['factorization_complexity'] / 100.0) # Complexity measure | |
| ] | |
| return float(np.mean(confidence_factors)) | |
| def _generate_entropy_signature(self) -> str: | |
| """Generate multi-source entropy signature""" | |
| temporal_entropy = int(datetime.now().timestamp() * 1e6) | |
| system_entropy = np.random.randint(0, 2**64) | |
| quantum_analog = hash(str(hashlib.sha3_256(str(temporal_entropy).encode()).digest())) | |
| combined = hashlib.sha3_512( | |
| f"{temporal_entropy}{system_entropy}{quantum_analog}".encode() | |
| ).hexdigest() | |
| return combined | |
| def verify_claim(self, claim: str, evidence: any, original_anchor: Dict) -> Dict[str, any]: | |
| """ | |
| Verify claim against original mathematical anchor | |
| """ | |
| new_anchor = self.create_verification_anchor(claim, evidence) | |
| # Mathematical verification | |
| hash_match = new_anchor['verification_hash'] == original_anchor['verification_hash'] | |
| prime_continuity = self._check_prime_continuity(original_anchor, new_anchor) | |
| integrity_correlation = self._compare_integrity_metrics(original_anchor, new_anchor) | |
| return { | |
| 'verified': hash_match and prime_continuity, | |
| 'confidence': new_anchor['confidence_score'], | |
| 'integrity_correlation': integrity_correlation, | |
| 'temporal_consistency': self._check_temporal_consistency(original_anchor, new_anchor), | |
| 'mathematical_continuity': prime_continuity | |
| } | |
| def _check_prime_continuity(self, anchor1: Dict, anchor2: Dict) -> bool: | |
| """Verify mathematical continuity between verification anchors""" | |
| primes1 = [anchor1['prime_core']['anchor_prime']] + anchor1['prime_core']['entropy_primes'] | |
| primes2 = [anchor2['prime_core']['anchor_prime']] + anchor2['prime_core']['entropy_primes'] | |
| # Check for mathematical relationships | |
| gcd_relationships = [np.gcd(p1, p2) for p1, p2 in zip(primes1, primes2)] | |
| return all(gcd == 1 for gcd in gcd_relationships) # Should be coprime | |
| def _compare_integrity_metrics(self, anchor1: Dict, anchor2: Dict) -> float: | |
| """Compare integrity metrics between verification sessions""" | |
| metrics1 = anchor1['integrity_metrics'] | |
| metrics2 = anchor2['integrity_metrics'] | |
| correlations = [] | |
| for key in metrics1: | |
| if key in metrics2: | |
| # Simple correlation analog for demonstration | |
| correlation = 1.0 - abs(metrics1[key] - metrics2[key]) / max(abs(metrics1[key]), 1e-9) | |
| correlations.append(max(0.0, correlation)) | |
| return float(np.mean(correlations)) if correlations else 0.0 | |
| def _check_temporal_consistency(self, anchor1: Dict, anchor2: Dict) -> bool: | |
| """Verify temporal consistency between verifications""" | |
| time1 = datetime.fromisoformat(anchor1['timestamp']) | |
| time2 = datetime.fromisoformat(anchor2['timestamp']) | |
| # Allow reasonable time difference for verification | |
| return abs((time2 - time1).total_seconds()) < 3600 # 1 hour window | |
| # Production-ready instantiation | |
| verification_engine = SovereignVerificationEngine() | |
| # Demonstration of mathematical verification system | |
| if __name__ == "__main__": | |
| # Create initial verification anchor | |
| claim = "Sovereign verification provides mathematical inevitability" | |
| evidence = {"framework": "Prime-based anchoring", "entropy_sources": 3} | |
| anchor = verification_engine.create_verification_anchor(claim, evidence) | |
| print(f"Verification Anchor Created: {anchor['verification_hash'][:16]}...") | |
| print(f"Confidence Score: {anchor['confidence_score']:.3f}") | |
| print(f"Integrity Metrics: {anchor['integrity_metrics']}") | |
| # Verify the claim | |
| verification = verification_engine.verify_claim(claim, evidence, anchor) | |
| print(f"\nVerification Result: {verification['verified']}") | |
| print(f"Integrity Correlation: {verification['integrity_correlation']:.3f}") |