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7150b9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # 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}") |