The system uses a three-layer encrypted balance architecture:

1. encrypted_user_balances: Map<user_id, encrypted_balance> - User's personal encrypted balance
2. user_balance_mappings: Map<encrypted_balance, actual_amount> - Public decryption lookup table
3. shared_balance_access: Map<auth_id, shared_encrypted_balance> - Authorization-specific balance sharing

// User's personal encryption key (PIN-based)
const user_key = persistentHash([pad(32, "user:balance:"), pin]);

// Balance encryption function
pure circuit encrypt_balance(balance: Uint<64>, encryption_key: Bytes<32>): Bytes<32> {
  const encrypted = persistentHash([persistentHash(balance), encryption_key]);
  return encrypted;
}

Issue: The user_balance_mappings ledger exposes a brute-forceable privacy layer.

• ✅ All balance amounts in the system (e.g., who has $100, $500, etc.)
• ✅ Balance distribution across users
• ✅ Number of users with each balance amount

• ❌ Direct userId → balance mapping (requires additional computation)

Risk Level: 🔴 HIGH for targeted attacks

Requirements:

• Know target user's userId (publicly visible in authorization requests)
• Computational resources for PIN brute force (4-6 digits = 10K-1M attempts)
• Access to user_balance_mappings public ledger data

Attack Process:

// Target: Find Alice's balance
const targetUserId = "alice";
const observedBalances = [100, 500, 1000, 5000]; // From user_balance_mappings values

for (const pin of generatePinCombinations()) {  // 0000-9999
  const userKey = persistentHash([pad(32, "user:balance:"), pin]);
  
  for (const balance of observedBalances) {
    const encrypted = encrypt_balance(balance, userKey);
    if (user_balance_mappings.has(encrypted)) {
      // SUCCESS: Found Alice's PIN and balance
      break;
    }
  }
}

Risk Level: 🟡 MEDIUM for common PINs

Method:

• Pre-compute encrypt_balance(balance, key) for common PINs and balance amounts
• Match against user_balance_mappings entries
• Discover balances for users with weak PINs ("1234", "0000", birth years)

Risk Level: 🟢 LOW privacy concern

Method:

• Analyze balance distribution patterns
• Identify economic trends or user behavior patterns
• Does not reveal individual user data

• ✅ Casual blockchain observers - Cannot easily link users to balances
• ✅ General analytics - Balance distribution analysis
• ✅ Strong PIN users - 6+ digit random PINs

• 🔴 Targeted investigations - Specific user balance discovery
• 🔴 Weak PIN users - Common patterns (1234, 0000, 1111)
• 🔴 Sophisticated attackers - With computational resources and user targeting

Implementation:

// Add unique salt per user
const user_salt = persistentHash([user_id, pad(32, "balance_salt")]);
const user_key = persistentHash([pad(32, "user:balance:"), pin, user_salt]);

Benefits:

• ✅ Prevents rainbow tables - Each user needs unique pre-computation
• ✅ Increases brute force cost - Attackers must target users individually
• ✅ Minimal code changes - Simple modification to existing system
• ✅ Backward compatible - Can migrate existing balances

Limitations:

• 🟡 Still vulnerable to targeted attacks - PIN brute force remains possible
• 🟡 Requires migration - Existing encrypted balances need re-encryption

Implementation:

// Remove user_balance_mappings entirely
// Store decrypted balances only in private state
witness user_balance(user_id: Bytes<32>): Uint<64>;

Benefits:

• ✅ Complete balance privacy - No public balance information
• ✅ No brute force vector - Cannot match encrypted keys to amounts
• ✅ Simple security model - Balances are private, period

Limitations:

• 🔴 Major architecture change - Requires significant contract modifications
• 🔴 Shared balance complexity - Authorization system needs redesign
• 🔴 Performance concerns - All balance operations require witness calls

Implementation:

• Use additive homomorphic encryption (e.g., Paillier cryptosystem)
• Allow balance operations without decryption
• Enable balance sharing without revealing exact amounts

Benefits:

• ✅ Strong cryptographic security - Computationally infeasible attacks
• ✅ Flexible operations - Can perform balance checks without decryption
• ✅ Future-proof - Resistant to computational advances

Limitations:

• 🔴 High complexity - Significant implementation overhead
• 🔴 Performance cost - Homomorphic operations are expensive
• 🔴 Library dependencies - May require external cryptographic libraries

Implementation:

• Use ZK-SNARKs for balance threshold proofs
• Users can prove "balance ≥ threshold" without revealing exact amount
• Eliminate need for encrypted balance sharing

Benefits:

• ✅ Perfect privacy - No balance information leakage
• ✅ Flexible verification - Supports complex balance conditions
• ✅ Cryptographically secure - Based on zero-knowledge proofs

Limitations:

• 🔴 Very high complexity - Requires ZK circuit development
• 🔴 Trusted setup - May require ceremony for some ZK systems
• 🔴 Performance overhead - Proof generation and verification costs

Target: Next release Solution: Implement user-specific salt (Solution 1) Effort: Low (1-2 days) Impact: Significantly reduces rainbow table and mass brute force attacks

Target: Future major version Solution: Evaluate Solution 2 (eliminate public mapping) Effort: High (1-2 weeks) Impact: Complete balance privacy, requires system redesign

Target: Future research Solutions: Homomorphic encryption or ZK proofs Effort: Very High (weeks/months) Impact: State-of-the-art privacy guarantees

• ✅ Adequate for disclosure use case - Disclosure is intentional and authorized
• ✅ Consistent security model - Same encryption patterns as authorization system
• ✅ Limited scope exposure - Only authorized parties gain access

1. Implement Solution 1 (salt) before adding disclosure feature
2. Apply same security model to disclosure permissions
3. Document privacy trade-offs for users

• PIN brute force attack simulation
• Rainbow table effectiveness testing
• Balance correlation analysis
• Authorization system security audit

• Encryption key generation consistency
• PIN validation and storage
• Ledger data exposure patterns
• Private state management security

Document Version: 1.0
Last Updated: 2025-01-16
Next Review: Before implementing selective balance disclosure feature