┌─────────────────────────────────────────────────────────────────────────────┐
│                    ZERO-KNOWLEDGE PROOF TYPES                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  1. ZK-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge)│
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Small proof size (succinct)                                      │   │
│  │ • Non-interactive (single message)                                  │   │
│  │ • Requires trusted setup                                            │   │
│  │ • Examples: Zcash, Tornado Cash                                    │   │
│  │ • Pros: Very small proofs, fast verification                        │   │
│  │ • Cons: Trusted setup, quantum vulnerable                           │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  2. ZK-STARKs (Zero-Knowledge Scalable Transparent Argument of Knowledge)  │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Scalable (fast for large computations)                           │   │
│  │ • Transparent (no trusted setup)                                    │   │
│  │ • Post-quantum secure                                                │   │
│  │ • Examples: StarkNet                                               │   │
│  │ • Pros: No trusted setup, scalable                                   │   │
│  │ • Cons: Larger proof size                                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  3. Bulletproofs                                                           │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Short proofs without trusted setup                               │   │
│  │ • Examples: Monero                                                 │   │
│  │ • Pros: No trusted setup                                             │   │
│  │ • Cons: Larger proofs, slower verification                         │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  4. PLONK (Permutation-based Proofs)                                       │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Universal trusted setup                                           │   │
│  │ • General-purpose                                                    │   │
│  │ • Examples: Aztec, Mina                                            │   │
│  │ • Pros: Universal setup, efficient                                   │   │
│  │ • Cons: Complexity                                                   │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    SMPC CONCEPT                                             │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Party A                     Party B                     Party C           │
│  ┌─────────────┐            ┌─────────────┐            ┌─────────────┐    │
│  │ Private Data │            │ Private Data │            │ Private Data │    │
│  │     (x)      │            │     (y)      │            │     (z)      │    │
│  └──────┬──────┘            └──────┬──────┘            └──────┬──────┘    │
│         │                          │                          │           │
│         └──────────────┬───────────┴───────────┬──────────────┘           │
│                        │                       │                          │
│                        v                       v                          │
│         ┌──────────────────────────────────────────────────────┐          │
│         │                    SMPC PROTOCOL                    │          │
│         │  • Shares are distributed                            │          │
│         │  • Computation occurs on shares                    │          │
│         │  • Result is reconstructed                           │          │
│         └──────────────────────────────────────────────────────┘          │
│                                    │                                        │
│                                    v                                        │
│                      ┌─────────────────────────┐                          │
│                      │   Result f(x, y, z)     │                          │
│                      └─────────────────────────┘                          │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 9, LESSON 5: PRIVACY-PRESERVING TECHNOLOGIES
# ===================================================================

import hashlib
import random
import time
from typing import Dict, List, Tuple
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("PRIVACY-PRESERVING TECHNOLOGIES")
print("="*70)

# ----------------------------------------------------------------
# PART A: ZERO-KNOWLEDGE PROOF SIMULATION (ADVANCED)
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Zero-Knowledge Proof Simulation (Range Proof)")
print("-"*60)

class RangeProof:
    """
    Simplified zero-knowledge range proof simulation.
    Proves a number is within a range without revealing it.
    """
    def __init__(self):
        self.secret = None
        self.range_min = 0
        self.range_max = 0
        self.proof_data = None
    
    def prove_range(self, secret: int, min_val: int, max_val: int) -> Dict:
        """
        Prove that secret is in [min_val, max_val] without revealing it.
        """
        self.secret = secret
        self.range_min = min_val
        self.range_max = max_val
        
        # Generate commitment
        nonce = random.randint(1000, 9999)
        commitment = hashlib.sha256(f"{secret}{nonce}".encode()).hexdigest()
        
        # Generate proof data
        proof = {
            'commitment': commitment,
            'nonce': nonce,
            'range_min': min_val,
            'range_max': max_val,
            'hash_plus': hashlib.sha256(f"{secret + 1}{nonce}".encode()).hexdigest(),
            'hash_minus': hashlib.sha256(f"{secret - 1}{nonce}".encode()).hexdigest()
        }
        self.proof_data = proof
        return proof
    
    def verify_proof(self, proof: Dict) -> Tuple[bool, str]:
        """
        Verify the range proof without knowing the secret.
        """
        nonce = proof['nonce']
        commitment = proof['commitment']
        min_val = proof['range_min']
        max_val = proof['range_max']
        hash_plus = proof['hash_plus']
        hash_minus = proof['hash_minus']
        
        # Check that commitment is consistent with range
        # In a real ZKP, this would use cryptographic verification
        # This is a simplified simulation
        
        # Simulate verification:
        # 1. Check that the secret is within range (by checking hash_plus and hash_minus)
        # 2. This simulates the verifier checking without knowing the secret
        
        # Simulate range check
        is_valid_range = True  # Would be cryptographically verified
        
        # Check that commitment is consistent
        # In real ZKP, this would verify the proof structure
        is_consistent = True
        
        if is_valid_range and is_consistent:
            return True, "Secret is within the range"
        else:
            return False, "Proof verification failed"

# Simulate range proof
zkp = RangeProof()

print("Range Proof Simulation:")

# Alice has a number (age) and wants to prove it's in a range
alice_age = 25
min_age = 18
max_age = 65

print(f"Alice is {alice_age} (she wants to prove she's between {min_age} and {max_age})")

# Alice creates a proof
proof = zkp.prove_range(alice_age, min_age, max_age)
print(f"Proof created: commitment = {proof['commitment'][:16]}...")

# Verifier checks the proof
is_valid, message = zkp.verify_proof(proof)
print(f"Verification: {message}")
print("The verifier does not know Alice's exact age")

# Test with out-of-range value
print("\nTesting out-of-range proof:")
zkp2 = RangeProof()
bob_age = 70  # Above range
proof2 = zkp2.prove_range(bob_age, min_age, max_age)
is_valid2, message2 = zkp2.verify_proof(proof2)
print(f"Bob's age: {bob_age} (he claims he's in range)")
print(f"Verification: {message2}")

# ----------------------------------------------------------------
# PART B: PRIVACY COIN COMPARISON
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Privacy Coin Comparison")
print("-"*60)

privacy_coin_data = {
    'Coin': ['Zcash', 'Monero', 'Dash', 'Grin', 'Beam', 'Secret Network'],
    'Privacy Mechanism': ['ZK-SNARKs', 'Ring CT', 'CoinJoin', 'Mimblewimble', 'Mimblewimble', 'Secret Contracts'],
    'Always Private': ['Optional', 'Yes', 'Optional', 'Yes', 'Yes', 'Yes'],
    'Transaction Size': ['Small', 'Large', 'Small', 'Medium', 'Medium', 'Medium'],
    'Scalability': ['High', 'Low', 'High', 'Medium', 'Medium', 'Medium']
}

privacy_df = pd.DataFrame(privacy_coin_data)
print(privacy_df.to_string(index=False))

# ----------------------------------------------------------------
# PART C: PRIVACY TECHNOLOGY COMPARISON
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Privacy Technology Comparison")
print("-"*60)

tech_data = {
    'Technology': ['ZK-SNARKs', 'ZK-STARKs', 'Bulletproofs', 'FHE', 'SMPC', 'TEE'],
    'Privacy Level': ['High', 'High', 'High', 'Very High', 'High', 'High'],
    'Performance': ['High', 'Medium', 'Low-Medium', 'Very Low', 'Medium', 'High'],
    'Complexity': ['High', 'High', 'Medium', 'Very High', 'High', 'Medium'],
    'Trusted Setup': ['Yes', 'No', 'No', 'No', 'No', 'No'],
    'Post-Quantum': ['No', 'Yes', 'No', 'Yes', 'Yes', 'No']
}

tech_df = pd.DataFrame(tech_data)
print(tech_df.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

ax1 = axes[0]
x = np.arange(len(tech_data['Technology']))
width = 0.25

ax1.bar(x - width, tech_data['Privacy Level'].map({'Very High': 5, 'High': 4, 'Medium': 3, 'Low': 2, 'Very Low': 1}), 
        width, label='Privacy', color='blue', alpha=0.7)
ax1.bar(x, tech_data['Performance'].map({'High': 5, 'Medium': 3, 'Low-Medium': 2, 'Very Low': 1}), 
        width, label='Performance', color='green', alpha=0.7)
ax1.bar(x + width, 6 - tech_data['Complexity'].map({'Very High': 1, 'High': 2, 'Medium': 3, 'Low': 4, 'Very Low': 5}), 
        width, label='Ease of Use', color='orange', alpha=0.7)

ax1.set_xlabel('Technology')
ax1.set_ylabel('Score (1-5)')
ax1.set_title('Privacy Technology Comparison')
ax1.set_xticks(x)
ax1.set_xticklabels(tech_data['Technology'], rotation=45, ha='right')
ax1.legend()
ax1.grid(True, alpha=0.3)

ax2 = axes[1]
trusted = [1 if t == 'Yes' else 0 for t in tech_data['Trusted Setup']]
post_quantum = [1 if t == 'Yes' else 0 for t in tech_data['Post-Quantum']]
ax2.bar(x - width/2, trusted, width, label='Trusted Setup Required', color='red', alpha=0.7)
ax2.bar(x + width/2, post_quantum, width, label='Post-Quantum Secure', color='green', alpha=0.7)
ax2.set_xlabel('Technology')
ax2.set_ylabel('Yes/No')
ax2.set_title('Trusted Setup and Post-Quantum Security')
ax2.set_xticks(x)
ax2.set_xticklabels(tech_data['Technology'], rotation=45, ha='right')
ax2.legend()
ax2.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('privacy_tech_comparison.png', dpi=300, bbox_inches='tight')
plt.show()
print("Privacy technology comparison saved as 'privacy_tech_comparison.png'")

# ----------------------------------------------------------------
# PART D: SUMMARY AND RECOMMENDATIONS
# -----------------------------------------------------------------

print("\n" + "="*70)
print("PART D: Summary and Recommendations")
print("="*70)

print("""
Privacy-Preserving Technologies – Key Takeaways:

1. Privacy is critical for many blockchain use cases (transactions, identity, healthcare).
2. ZKP types: ZK-SNARKs (succinct, trusted setup), ZK-STARKs (scalable, transparent), Bulletproofs.
3. Privacy-preserving computation: homomorphic encryption, SMPC, TEE.
4. Privacy coins: Zcash (ZK-SNARKs), Monero (Ring CT), Dash (CoinJoin), Grin/Beam (Mimblewimble).
5. Regulatory considerations: privacy technologies face regulatory scrutiny.
6. Trade-offs: privacy vs transparency, performance vs privacy, complexity vs adoption.

Recommendations:
  - Use ZKPs for privacy-preserving verification.
  - Consider privacy coins for confidential transactions.
  - Evaluate regulatory compliance requirements.
  - Balance privacy with transparency needs.
  - Stay updated on privacy technology developments.
  - Consider hybrid approaches (private + public).
""")

print("="*70)
print("END OF LESSON 5 – MODULE 9")
print("="*70)