[Feature] Complete Zero-Knowledge Proof Integration for Privacy-Preserving Meter Readings and Tamper Detection

[KarenZita01]

Description

The zk_tests.rs (3KB) contains test structures for zero-knowledge proof integration, but there is no actual ZK proof verification logic. The tamper_detection.rs (1.3KB) is a stub with minimal implementation. These modules suggest planned ZK-verified meter readings and tamper-proof reporting, but the implementation is not yet complete. For a utility metering protocol, cryptographic guarantee of meter reading integrity is critical.

ZK/tamper gaps:

• zk_tests.rs contains test placeholders but no actual ZK verification
• tamper_detection.rs defines TamperEvent struct but has no detection logic
• No Merkle tree or accumulator for batch meter reading verification
• No zk-SNARK/stark verifier integration (needs Groth16 or PLONK verifier on Soroban)
• Meter readings are stored as plain values with only ED25519 signatures; no zero-knowledge privacy for consumption data
• No proof-of-location for meter registration (preventing geolocation spoofing)

Technical Context & Impact

• Affected Components/Files: contracts/utility_contracts/src/zk_tests.rs, contracts/utility_contracts/src/tamper_detection.rs, contracts/utility_contracts/src/lib.rs
• Impact: Privacy, Data Integrity, Regulatory Compliance

Step-by-Step Implementation Guide

1. Research Soroban ZK capability: Evaluate available ZK verifiers for Soroban (currently experimental); explore groth16-solana or bellman integration via Soroban host functions
2. Design ZK circuit for meter readings: Define public inputs (meter_id, timestamp, commitment) and private inputs (actual consumption, user_id)
3. Implement verify_zk_proof(proof: Bytes, public_inputs: Vec<Bytes>) -> bool: Start with a placeholder that accepts proof format and calls verifier, with graceful fallback to non-ZK mode until verifier is available
4. Complete tamper detection: Implement detect_tamper(meter_id, readings: Vec<SignedReading>) -> TamperDetectionResult:
   • Check reading sequence: validate readings are monotonically increasing
   • Check reading frequency: flag if readings arrive too frequently (suggests replay)
   • Check cross-meter correlation: flag anomalies vs. neighborhood meters
5. Add Merkle tree for batch verification: Store DataKey::ReadingMerkleRoot(period) for batch proving all readings in a billing period
6. Add proof-of-location: Optional register_meter_location(meter_id, geo_hash, signature) using blockchain-based geolocation oracle
7. Privacy mode: Allow meters to submit H(consumption || salt) instead of raw consumption, revealing only on billing via ZK proof

Verification & Testing Steps

1. Run existing ZK tests: cargo test zk_tests (should pass with placeholder)
2. Create mock ZK verifier for testing: test proof generation -> verification roundtrip
3. Test tamper detection with normal readings (no alert) vs anomalous readings (alert raised)
4. Test Merkle tree: submit 100 readings -> compute root -> verify individual proof
5. Test privacy mode: encrypted consumption submitted -> admin cannot read individual consumption -> ZK proof verifies billing total
6. Run full test suite: cargo test --package utility_contracts