Setting Up a Privacy-Preserving Supply Chain Ledger

A privacy-preserving supply chain ledger is a blockchain-based system that records the provenance and state of goods while keeping sensitive commercial data—like pricing, supplier identities, or quality test results—confidential. Unlike a transparent public ledger, it uses cryptographic primitives such as zero-knowledge proofs (ZKPs) and commitment schemes to allow participants to prove the validity of transactions (e.g., a shipment's authenticity) without revealing the underlying data. This architecture is critical for enterprise adoption, as it balances the need for transparency in traceability with the confidentiality required for competitive business operations.

The core components of such a system include a permissioned blockchain or a layer-2 network (like Polygon zkEVM or a custom Avalanche subnet) for consensus, a smart contract to manage state commitments, and a client-side SDK for generating and verifying proofs. Data is stored off-chain in a decentralized file system (e.g., IPFS or Arweave), with only a cryptographic hash—a commitment—posted on-chain. When a participant needs to verify a claim, such as "this batch was stored below 5°C," they request a ZK proof from the data holder, which is then verified against the on-chain commitment without exposing the raw temperature logs.

To set up a basic ledger, start by defining your data schema and privacy requirements. For a pharmaceutical supply chain, you might create a Batch struct with private fields (manufacturerId, temperatureLog) and public fields (batchId, timestamp). Using a library like Circom or SnarkJS, you design a circuit that proves a temperature log never exceeded a threshold. The verification key for this circuit is deployed alongside your smart contract. Participants use a client application to generate proofs from their private data, submitting only the proof and public outputs to the blockchain, thus keeping the sensitive log hidden.

Here is a simplified example of a smart contract function for verifying a delivery proof using the Semaphore ZK framework, which is designed for anonymous signaling:

solidityCopy
function verifyDelivery(
    uint256 batchId,
    uint256[8] calldata proof,
    uint256 root,
    uint256 nullifierHash
) public {
    require(verifier.verifyProof(proof, [root, nullifierHash]), "Invalid proof");
    _recordVerifiedDelivery(batchId, nullifierHash);
}

This function checks a ZK proof that validates a participant possesses valid credentials for a batch without revealing who they are, preventing competitors from mapping your supply network.

Key operational challenges include managing oracle data for real-world events and ensuring interoperability with existing ERP systems. Solutions often involve using TLSNOTARY or Chainlink oracles to attest to IoT sensor data before it's committed, and employing standard APIs (like GS1's EPCIS) for data formatting. The system must also be designed for selective disclosure, where a participant can voluntarily reveal specific data fields to an auditor via a temporary key, providing regulatory compliance without full transparency.

For production deployment, consider using specialized frameworks like Aztec Network for private smart contracts or Hyperledger Fabric with its private data collections. The end goal is a system where stakeholders can cryptographically audit the chain of custody and compliance, while individual businesses retain control over their proprietary information. This enables trustless collaboration in complex supply chains, from automotive parts to organic food, without sacrificing competitive advantage.

To follow this guide, you need a foundational understanding of blockchain concepts like transactions, blocks, and consensus. Familiarity with smart contract development using Solidity and the Ethereum Virtual Machine (EVM) is essential, as we'll use it as our base layer. You should have Node.js and npm/yarn installed, and experience with a command-line interface. For development, we'll use the Hardhat framework for compiling, testing, and deploying contracts, and TypeScript for writing our off-chain logic. A basic grasp of cryptography, particularly public-key encryption and hash functions, will be helpful for understanding the privacy mechanisms.

The system architecture is a hybrid on-chain/off-chain model. The on-chain component is a set of Ethereum smart contracts that act as the immutable, permissioned root of trust. This layer manages participant identities, stores cryptographic commitments (hashes) of supply chain events, and handles dispute resolution. Sensitive data, like exact product quantities, pricing, or proprietary details, is never stored directly on-chain. Instead, this private data is encrypted and stored in a decentralized off-chain storage layer, such as IPFS or Ceramic Network, with only the content identifier (CID) or a zero-knowledge proof posted to the ledger.

For privacy, we implement Zero-Knowledge Proofs (ZKPs) using a library like Circom and snarkjs. When a participant needs to prove a claim (e.g., "this shipment's temperature never exceeded 8°C") without revealing the underlying sensor data, they generate a ZKP off-chain. The verifier contract on-chain can then validate this proof. Access control is managed through a registry contract that uses ERC-721 tokens as membership badges, granting rights to view specific data streams or submit events for a particular supply chain lane.

The off-chain orchestrator is a crucial backend service, typically built with Node.js. It listens for on-chain events, manages private keys for data encryption, interacts with off-chain storage, generates ZKPs, and submits transactions back to the blockchain. This service must be run by a trusted party or a decentralized network of oracles. The end-user interface can be a dApp frontend (using ethers.js or viem) that allows participants to view verified events and proofs pertinent to their role, creating a seamless flow from private data generation to public, verifiable attestation.

The foundation of a privacy-preserving supply chain ledger is a commitment scheme. Instead of storing plaintext data like product origin or batch numbers on-chain, we store cryptographic hashes or commitments. A commitment is a one-way function output that binds to a specific data point without revealing it. For each supply chain event (e.g., 'manufactured', 'shipped'), the responsible party generates a commitment using a secret random value (a nonce). Only the final commitment hash is stored on the public ledger, creating an immutable, private record. This approach ensures data confidentiality while maintaining a tamper-proof audit trail.

To enable verification without exposing secrets, we implement zero-knowledge proofs (ZKPs). When a downstream participant, like a retailer, needs to verify a product's authenticity, they request proof from the supplier. The supplier generates a ZKP, such as a zk-SNARK, that cryptographically demonstrates knowledge of the original data and nonce that correspond to the public commitment, without revealing them. The verifier (e.g., a smart contract) can check this proof against the on-chain commitment to confirm the claim's validity. Libraries like Circom or Halo2 are used to design the arithmetic circuits that define the proof statements for supply chain logic.

The core smart contract must manage the lifecycle of these commitments and proofs. A typical SupplyChainLedger contract will have functions to: createCommitment(bytes32 commitmentHash) for logging new events, verifyProof(bytes calldata proof, bytes32 publicInput) for on-chain validation, and getProvenance(bytes32 productId) to return the sequence of commitments for a product. Access control via modifiers like onlyAuthorizedNode is critical to prevent unauthorized parties from posting data. The contract state is minimal, storing only public hashes and verification keys, keeping gas costs manageable.

For practical interoperability, the system must handle off-chain data storage. The private data (e.g., supplier invoices, quality certificates) and the large ZKP artifacts are stored off-chain using solutions like IPFS or Ceramic Network. The on-chain commitment then includes the content identifier (CID) of this off-chain data. The proof verification logic can be designed to confirm that the prover knows data corresponding to that specific CID. This hybrid architecture balances the transparency and security of the blockchain with the scalability needed for complex supply chain documents.

Finally, consider upgradeability and standards. Use a proxy pattern (e.g., Transparent Proxy) for future improvements to the verification logic. Adhere to emerging standards for verifiable credentials, such as W3C Verifiable Credentials, to ensure compatibility with other systems. By combining commitments, zero-knowledge proofs, and careful contract design, you can build a supply chain ledger that provides the necessary trust and auditability for businesses while rigorously protecting their competitive information.

The core of a privacy-preserving supply chain ledger is a zk-SNARK circuit that encodes your business rules. Using a framework like Circom, you define constraints that prove a transaction is valid. For example, a circuit can verify that a shipment's declared weight matches a hashed bill of lading, or that a payment release is triggered only after three separate approvals are logged, all without revealing the approvers' identities or the payment amount. The circuit's outputs are a proof and public signals.

To implement this, you first write your circuit logic. Below is a simplified Circom example for a rule requiring a minimum product temperature during transit. The circuit takes a private input actualTemp and a public minTemp, proving the condition was met.

circomCopy
pragma circom 2.0.0;
template TemperatureCheck() {
    signal input minTemp; // Public: minimum allowed
    signal private input actualTemp; // Private: sensor reading
    signal output isValid;
    // The constraint: actualTemp must be >= minTemp
    component gt = GreaterEqThan(32);
    gt.in[0] <== actualTemp;
    gt.in[1] <== minTemp;
    isValid <== gt.out;
}
component main = TemperatureCheck();

After compiling, you generate the proving and verification keys, which are deployed with your ledger's smart contract.

The on-chain verification contract, typically written in Solidity for Ethereum or Solana's Anchor framework, is lightweight. It only needs to verify the zk-SNARK proof against the verification key and the public signals. For instance, an EscrowRelease contract would call a verifier like ZKVerifier.verifyProof(proof, publicSignals) where publicSignals might contain a transaction ID. A true result confirms all private business rules were satisfied, triggering the contract's release function. This decouples complex logic verification from the chain.

Integrating this flow requires a backend prover service. When a supply chain event occurs (e.g., a delivery scan), your off-chain service gathers the private data (location, timestamp, signatures), generates the witness, and creates the proof using the proving key. This proof and the necessary public signals are then submitted to the blockchain. This architecture ensures sensitive operational data—like supplier costs, exact geo-coordinates, or quality inspection details—never touches the public ledger, while providing cryptographic assurance of compliance.

For production, consider using zkRollup architectures or specific L2s like Aztec or Polygon zkEVM to batch proofs and reduce cost. Tools like SnarkJS for proof generation and Hardhat plugins for testing are essential. Always audit your circuits with tools like Picus or Verilog to prevent logical flaws that could compromise the system's integrity. The result is a supply chain ledger where participants can verify the entire history's validity without accessing its confidential contents.

The core challenge is creating a trustless bridge between private enterprise systems (ERP, WMS) and the public blockchain. A middleware service acts as an oracle, listening for events from IoT sensors and ERP APIs. When a shipment's temperature exceeds a threshold or a pallet is scanned at a warehouse gate, this service cryptographically signs the event data and submits a hash to the ledger. This creates an immutable, timestamped proof-of-event on-chain, while keeping the raw, potentially sensitive sensor data off-chain in a decentralized storage solution like IPFS or Arweave, referenced by the hash.

For ERP integration, focus on event-driven updates rather than continuous synchronization. Use webhooks or message queues (e.g., RabbitMQ, Apache Kafka) to capture state changes like purchase order creation, invoice issuance, or inventory adjustments. Your middleware should transform this data into a standardized schema, generate a zero-knowledge proof (ZKP) using a framework like Circom or Noir to validate the business logic (e.g., "invoice total matches sum of line items") without revealing the line items themselves, and then post the proof and resulting hash to the smart contract. This preserves operational privacy.

Implementing this requires careful oracle design to prevent data manipulation. The middleware's signing key must be securely managed using HSMs or cloud KMS. For high-assurance systems, use a decentralized oracle network like Chainlink, where multiple nodes fetch and attest to the data, with consensus required before the ledger is updated. This mitigates the risk of a single point of failure or corruption. The smart contract should verify the oracle signature or ZKP before accepting the state update.

Here is a simplified Node.js example of an oracle listener for an IoT temperature event, using Ethereum and IPFS:

javascriptCopy
// Pseudo-code for middleware service
import { ethers } from 'ethers';
import { create } from 'ipfs-http-client';

async function logTemperatureEvent(sensorId, reading, timestamp) {
  // 1. Store raw data off-chain
  const ipfs = create({ host: 'ipfs.infura.io', port: 5001, protocol: 'https' });
  const rawData = { sensorId, reading, timestamp };
  const { cid } = await ipfs.add(JSON.stringify(rawData));
  const ipfsHash = cid.toString();

  // 2. Create hash for on-chain proof
  const eventHash = ethers.keccak256(
    ethers.toUtf8Bytes(`${sensorId}-${reading}-${timestamp}-${ipfsHash}`)
  );

  // 3. Sign the hash with oracle private key (securely stored)
  const wallet = new ethers.Wallet(process.env.ORACLE_PK);
  const signature = await wallet.signMessage(ethers.getBytes(eventHash));

  // 4. Submit to smart contract
  const contract = new ethers.Contract(contractAddress, abi, wallet);
  await contract.recordEvent(sensorId, eventHash, signature, ipfsHash);
}

Finally, establish idempotency and error handling. Each external event should have a unique ID (like an ERP transaction ID or IoT device UUID) to prevent duplicate on-chain records. The middleware must queue failed transactions and implement retry logic with exponential backoff. Monitor the health of all connections—ERP APIs, IoT message brokers, and blockchain RPC nodes—to ensure the supply chain ledger remains a reliable system of record. This backend integration layer turns isolated data silos into components of a coherent, privacy-preserving verifiable track-and-trace system.

A privacy-preserving supply chain ledger must balance two competing needs: the operational privacy of participants and the regulatory requirement for auditability. Traditional blockchains like Ethereum make all transaction data public, exposing sensitive business logic. The solution is selective disclosure, a cryptographic protocol that allows a participant (e.g., a supplier) to reveal a specific, verifiable claim about their data to an auditor, while keeping all other information confidential. This is typically achieved using zero-knowledge proofs (ZKPs), which generate a cryptographic proof that a statement is true without revealing the underlying data.

To implement this, you first need to structure your ledger data around verifiable credentials or commitments. Instead of storing plaintext records like {order: 123, item: "microchip", price: 5.50}, you store a cryptographic commitment, such as a Pedersen Commitment or a hash. The plaintext data is kept off-chain by the data owner. When an auditor needs to verify that "the price for order 123 was under $10," the supplier generates a ZKP (e.g., using the Circom language and the SnarkJS library) that proves the committed data satisfies that condition, without revealing the exact price.

A practical setup involves a circuit written in a ZKP domain-specific language. For our price audit example, a Circom circuit would take the secret price and the public maximum threshold as inputs, and output a proof that price < threshold. The supplier runs this circuit with their private data to generate a proof, which is then sent to the auditor. The auditor, who only knows the public threshold and the on-chain commitment, can verify the proof's validity using a verifying key. Popular frameworks for developing such systems include Circom with SnarkJS for proof generation, or zk-SNARKs libraries like arkworks in Rust.

The on-chain component acts as a verification registry. Smart contracts (e.g., on Ethereum, Polygon, or a dedicated appchain) store the data commitments and the verifier smart contract logic. When an auditor receives a proof, they call this verifier contract, which checks the proof against the stored commitment. A successful verification emits an event, providing an immutable, auditable record that the disclosure occurred and was valid. This architecture ensures data minimization for audits and creates a non-repudiable log of verification requests and outcomes.

Key considerations for production include choosing the right proof system for your throughput needs (zk-SNARKs for succinct proofs, zk-STARKs for no trusted setup), managing the trusted setup ceremony for SNARKs, and designing a robust off-chain data availability solution for the plaintext records. Projects like Semaphore for anonymous signaling or zk-creds for verifiable credentials offer foundational patterns. By implementing selective disclosure, enterprises can leverage blockchain's integrity for supply chain provenance while maintaining competitive confidentiality and complying with regulations like GDPR.

You have now implemented a foundational system that uses zero-knowledge proofs (ZKPs) to verify supply chain events—like product origin, temperature logs, or customs clearance—without revealing the underlying sensitive data. By leveraging a zk-SNARK circuit, you can generate a cryptographic proof that a valid event occurred according to your business rules. This proof, along with a public commitment to the data, is stored on-chain, creating an immutable, verifiable audit trail. The raw data itself remains off-chain, controlled by the data owner.

The next step is to implement the selective disclosure mechanism. Using a library like Semaphore or zk-kit, you can allow a verifier (e.g., a customs agency) to request proof for a specific claim, such as "product temperature never exceeded 8°C." Your system would then generate a ZKP for that specific predicate, proving the claim is true based on the private data, without leaking any other information. This balances regulatory compliance with competitive secrecy.

For production deployment, consider these key areas: oracle integration for real-world data (using Chainlink or API3), scalability solutions like zkRollups (StarkNet, zkSync) to batch proofs and reduce costs, and interoperability standards such as W3C Verifiable Credentials to ensure your attestations are portable across ecosystems. Audit your ZK circuits with tools like Picus or Veridise before mainnet use.

To extend the system, explore adding multi-party computation (MPC) for scenarios requiring input from several distrustful parties, like a joint-venture shipment. You could also integrate decentralized identifiers (DIDs) to create cryptographically verifiable identities for each supply chain participant, moving beyond simple Ethereum addresses to a more portable identity layer.

The code and concepts shown provide a template. The real work lies in tailoring the business logic within your ZK circuits to match your specific supply chain's compliance, operational, and partnership requirements. Start with a pilot on a testnet, engage with stakeholders for feedback on the disclosure flows, and iterate.