Setting Up a Zero-Knowledge Proof System for Private Compliance Reporting

Private compliance reporting allows entities to prove they meet regulatory requirements—like transaction limits or KYC checks—without exposing the underlying sensitive data. Zero-knowledge proofs (ZKPs) are the cryptographic primitive that makes this possible. A ZKP system for compliance typically involves three core components: a prover that generates proofs from private data, a verifier that checks the proofs, and a circuit that encodes the compliance logic. This tutorial will walk through setting up a basic system using the Circom language for circuit design and the SnarkJS library for proof generation and verification.

The first step is defining the compliance logic as an arithmetic circuit. For example, to prove a transaction value is under a $10,000 threshold without revealing the amount, you would write a Circom circuit. The circuit takes a private input amount and a public input threshold, and outputs 1 only if amount < threshold. Here's a simplified template:

circomCopy
template ComplianceCheck() {
    signal private input amount;
    signal input threshold;
    signal output isCompliant;

    // Constraint: isCompliant is 1 if amount < threshold, 0 otherwise.
    // This uses a comparison component from a Circom library.
    component lt = LessThan(32); // 32-bit comparison
    lt.in[0] <== amount;
    lt.in[1] <== threshold;
    isCompliant <== lt.out;
}

After writing the circuit, you compile it to generate the R1CS constraint system and the wasm code for the witness calculator.

Next, you need a trusted setup to generate the proving and verification keys. For production, this requires a secure multi-party ceremony (like the Perpetual Powers of Tau). For development, you can use a local, non-secure setup with SnarkJS. The commands snarkjs powersoftau new and snarkjs plonk setup will generate a proving_key.zkey and verification_key.json. The prover then uses these keys, along with a valid witness (the private data satisfying the circuit), to generate a proof. The witness is computed by running the compiled wasm with the private inputs.

The final component is the verifier, which can be integrated into an on-chain smart contract or an off-chain service. Using SnarkJS, you generate a Solidity verifier contract from the verification key with snarkjs zkey export solidityverifier. This contract has a single verifyProof function. Any party—like a regulator or a protocol—can submit the proof and public inputs to this contract. If the proof is valid, the contract confirms the compliance statement is true, all without learning the private amount. This pattern is used by protocols like Aztec for private rollups and Tornado Cash (pre-sanctions) for privacy pools.

When deploying such a system, critical considerations include the choice of proof system (Groth16 for small proofs, PLONK for universal setup), circuit complexity (which impacts gas costs for on-chain verification), and the security of the trusted setup. For auditing, you must ensure the circuit correctly encodes the business logic, as bugs are irreversible. Tools like zkREPL and Picus Security's audit services can help. This setup provides a foundation for building applications that require data minimization under regulations like GDPR or for submitting private reports to institutions like the SEC.

A zero-knowledge proof (ZKP) system for compliance reporting allows an entity to prove the validity of its financial or operational data to a regulator without revealing the underlying sensitive information. This requires a specific technical stack. The core prerequisites include a development environment with Rust (version 1.70+) or C++ for performance-critical circuits, Node.js (v18+) for application logic, and a package manager like npm or yarn. You will also need to install a ZK-specific toolkit; Circom with snarkjs is a popular choice for designing arithmetic circuits and generating proofs, while Halo2 (in Rust) offers advanced features for recursive proofs.

The system architecture separates the prover (the reporting entity), the verifier (the regulator), and the trusted setup. The prover's system must compile the compliance logic into an arithmetic circuit, generate a proving key during the trusted setup phase, and then create a proof using its private data. This proof is a small cryptographic string. The verifier only needs the circuit's verification key and the public outputs (e.g., "all transactions are under $10,000") to validate the proof. A common pattern is to implement the prover logic as a backend service using a framework like Express.js, which accepts private data, calls the ZK proving library, and outputs the proof.

For development and testing, you need access to a trusted setup ceremony or a local simulation. In production, systems like Perpetual Powers of Tau provide a universal, reusable setup for Groth16 proofs, which is crucial for security. Architecturally, consider how private inputs are secured: they should be injected at runtime, never hardcoded, and ideally managed via secure enclaves or hardware security modules (HSMs) in high-stakes environments. The public outputs and the generated proof are typically posted to a blockchain (e.g., Ethereum, Polygon) or transmitted via a secure API, creating an immutable, verifiable record for the regulator.

Performance is a key consideration. Generating ZK proofs is computationally intensive. For production, the prover will require a high-performance machine with a multi-core CPU (e.g., AWS c6i.8xlarge or equivalent) and ample RAM (32GB+). The verification step, in contrast, is lightweight and can run on a standard server or even a smart contract. When designing the circuit, optimize for the number of constraints—each logical operation adds to proving time. Use techniques like custom gates (in Halo2) or efficient hash functions (Poseidon) to reduce circuit size and cost.

Zero-knowledge proofs (ZKPs) allow one party (the prover) to convince another (the verifier) that a statement is true without revealing any information beyond the validity of the statement itself. For compliance reporting, this enables a financial institution to prove transactions adhere to regulations—like sanctions screening or capital requirements—without disclosing the underlying customer data, transaction amounts, or counterparties. This privacy-preserving verification is achieved through cryptographic protocols like zk-SNARKs (Succinct Non-interactive Arguments of Knowledge) or zk-STARKs, which generate a small, easily verifiable proof.

To set up a basic system, you first define the compliance rule as a computational constraint system or an arithmetic circuit. For example, a rule stating "no transaction exceeds $10,000" would be encoded into a circuit that takes a private input tx_amount and a public parameter limit=10000 and outputs 1 if tx_amount <= limit. Using a library like circom (for circuit compilation) or ZoKrates (a toolbox for ZKPs on Ethereum), you write this logic. The circuit is then compiled into a format that ZKP backends like snarkjs or arkworks can use to generate proving and verification keys.

The development workflow involves three core stages: setup, proof generation, and verification. In the trusted setup phase, you use the compiled circuit to generate a proving key (used to create proofs) and a verification key (used to check them). For production, this often requires a secure multi-party ceremony to decentralize trust. Next, the proving phase happens on the client side: the prover uses the proving key, the private witness data (e.g., the actual tx_amount), and the public inputs to generate a succinct proof. Finally, the verification phase can be performed on-chain by a smart contract or off-chain by a regulator's server, using only the verification key, the proof, and the public statement.

Here is a simplified conceptual flow using a circom and snarkjs stack for a transaction limit check:

circomCopy
pragma circom 2.0.0;
template TransactionLimit() {
    signal input txAmount; // Private
    signal input limit;    // Public
    signal output isValid;
    isValid <== txAmount <= limit ? 1 : 0;
}
component main = TransactionLimit();

After compiling this circuit, you would run snarkjs to perform the setup, generate a proof for a specific txAmount, and create a verifier contract. The regulator would only receive the proof and the public limit, never the private txAmount.

Key considerations for a production system include selecting the right proving system (zk-SNARKs require a trusted setup but have small proof sizes; zk-STARKs are transparent but generate larger proofs), managing the computational cost of proof generation, and integrating the verifier with existing compliance infrastructure. For on-chain verification, gas costs on Ethereum for SNARK verifiers can be significant, making Layer 2 solutions or dedicated verification chains like Mina or Polygon zkEVM attractive. Off-chain, you can use faster proving schemes and deliver proofs via APIs to regulatory bodies.

Real-world implementations are emerging. Aztec Network offers a zk-rollup with private compliance features. Manta Network uses zk-SNARKs for private payments with regulatory proofs. When architecting your system, audit the circuit logic thoroughly—bugs here are critical—and consider using standardized circuits from libraries like zklib for common financial primitives. The end goal is a system where compliance is automated, auditable, and private, shifting the paradigm from bulk data submission to proof-of-compliance submission.

A zero-knowledge circuit is a set of arithmetic constraints that define a correct computation. For compliance, this circuit must encode rules like verifying a user's accredited investor status, ensuring a transaction is below a jurisdictional limit, or confirming the source of funds is not on a sanctions list. The circuit takes private inputs (e.g., user balance, transaction amount) and public inputs (e.g., a regulatory threshold) and outputs a proof that the private data satisfies the public rule. We use Circom (Circuit Compiler) because it's a popular domain-specific language for writing ZK circuits that compile to R1CS (Rank-1 Constraint System), the format used by proving systems like Groth16.

Let's design a simple circuit to prove a transaction is under a $10,000 reporting threshold without revealing the exact amount. We define a template ComplianceCircuit with a private signal amount and a public signal threshold. The core constraint is amount < threshold. In Circom, we must express this using arithmetic operations. We use a trick: prove threshold - amount - 1 >= 0 by creating a non-negative auxiliary signal. Here's the basic structure:

circomCopy
pragma circom 2.0.0;
template ComplianceCircuit() {
    signal input amount; // Private
    signal input threshold; // Public
    signal output verified;
    // Ensure amount is less than threshold
    signal diff <-- threshold - amount;
    signal diffMinusOne <-- diff - 1;
    diffMinusOne === 0; // Constraint: (threshold - amount - 1) must be 0
    verified <-- 1;
}
component main = ComplianceCircuit();

This circuit will only generate a valid proof if amount is strictly less than threshold.

For real-world use, circuits need greater complexity and security. Our simple example has a flaw: the <-- operator assigns a value without generating a constraint, creating a vulnerability where a prover could lie. We must replace it with the constraint-generating === operator and use a comparator circuit from a trusted library like circomlib. A robust implementation would import LessThan from circomlib and use it to output a bit (1 or 0) proving the relationship. Furthermore, you must add constraints to ensure all signals are within an expected range (e.g., non-negative, within 64 bits) to prevent overflow attacks. Always audit and test circuits with tools like snarkjs to verify the constraints match the intended logic before proceeding to proof generation.

A trusted setup ceremony is a critical, one-time procedure that generates the public parameters (proving and verification keys) for your zk-SNARK circuit. For systems like Groth16, this involves creating a Structured Reference String (SRS). The ceremony uses multi-party computation (MPC) to ensure that if at least one participant is honest and destroys their toxic waste (the secret randomness), the system's security is maintained. For development and testing, you can use a local, insecure setup. Using snarkjs, you initiate this with snarkjs powersoftau new bn128 12 pot12_0000.ptau. This creates an initial ptau file with a power of 12, sufficient for circuits with up to 2^12 constraints.

Next, you must compile your circuit into a format the proving system understands. Using circom, you compile your .circom file to R1CS (Rank-1 Constraint System) and generate a witness. The commands are circom compliance.circom --r1cs --wasm and snarkjs wtns calculate compliance.wasm input.json witness.wtns. The R1CS file mathematically represents your circuit, while the witness file contains a specific solution to it using your private inputs. This witness is what you will later prove knowledge of, without revealing it.

With the circuit compiled, you finalize the trusted setup specifically for your circuit. Using the ptau file from the initial phase, you run snarkjs groth16 setup compliance.r1cs pot12_final.ptau compliance_0000.zkey to create a .zkey file containing the proving key and verification key. To make this key fully trusted, you contribute randomness in a phase 2 ceremony: snarkjs zkey contribute compliance_0000.zkey compliance_0001.zkey. Finally, you export the verification key as a JSON file with snarkjs zkey export verificationkey compliance_0001.zkey verification_key.json. This verification_key.json is what the verifier will use.

Generating a proof is the operational step where a user proves they possess a valid witness. Using the final .zkey and the .wtns file, you execute snarkjs groth16 prove compliance_0001.zkey witness.wtns proof.json public.json. This command outputs two files: proof.json contains the actual cryptographic proof (three elliptic curve points), and public.json contains the public inputs and outputs of the circuit, which are revealed on-chain. The proof generation process is computationally intensive and is typically performed client-side by the user's device or a dedicated prover service.

Finally, you must verify the proof to ensure it is valid. This can be done off-chain for testing using snarkjs groth16 verify verification_key.json public.json proof.json. For on-chain compliance, the verification key is embedded into a verifier smart contract. You generate this Solidity contract with snarkjs zkey export solidityverifier compliance_0001.zkey verifier.sol. Deploying this contract to your chosen blockchain (e.g., Ethereum, Polygon, Arbitrum) creates a immutable verification endpoint. Any party can then call the verifyProof function, submitting the proof and public signals, to cryptographically confirm the report's validity without learning its private contents.

Once a financial institution has generated a zero-knowledge proof (ZKP) attesting to a compliance rule—like proving a transaction volume is under a threshold without revealing individual amounts—the regulator needs a mechanism to verify it. This step involves the regulator running a verification algorithm on the publicly submitted proof and the associated public inputs. In a system like Circom and SnarkJS, the verification key (often generated during a trusted setup) is used. The core action is a cryptographic check that confirms the proof is valid and was generated from the correct private witness data, without the regulator ever seeing that data.

The technical integration typically involves a smart contract on a public blockchain like Ethereum. The institution submits the ZKP (as a set of elliptic curve points) and public parameters to a Verifier contract. This contract contains the verification logic compiled from the original circuit. A regulator, or any party, can then call a function like verifyProof(proof, publicInputs) which returns a simple boolean. This on-chain verification provides a cryptographically secure, immutable audit trail. For example, using the Groth16 proving scheme, the Solidity verifier checks pairings to validate the proof's integrity.

Setting up this verification requires the regulator to have access to the correct verification key. In a production system, this key is derived from a trusted setup ceremony (like Perpetual Powers of Tau) specific to the compliance circuit. The key is then embedded into the verifier contract. It's critical that the circuit's constraints and the public input structure are standardized and agreed upon by both parties to ensure the proof validates the intended statement. Tools like snarkjszkey export verificationkey can generate the necessary JSON key for contract deployment.

For regulators, the workflow is straightforward: monitor for new proof submissions on-chain, optionally using an indexer or subgraph, and trigger the verification call. The result is a definitive pass/fail for the compliance claim. This model shifts the regulatory burden from continuous data auditing to spot-checking cryptographic guarantees. It enables scalable oversight for regulations like Travel Rule (FATF) or capital adequacy requirements, where the proof demonstrates aggregate compliance across thousands of private transactions.

The system you've built demonstrates a foundational pattern for privacy-preserving compliance. By using a zk-SNARK circuit written in Circom, you can prove that a set of private transactions adheres to regulatory rules—like a daily volume limit—without revealing the underlying data. The verifier smart contract, deployed on a chain like Ethereum or Polygon, allows any third party to cryptographically confirm the report's validity with a single function call. This moves compliance from a process of data submission to one of proof submission.

For production deployment, several critical next steps are required. First, conduct a formal security audit of both your Circom circuit and Solidity verifier. Tools like Ecne or Veridise can help analyze circuit logic. Second, implement a robust prover backend service using snarkjs or a Rust-based framework like arkworks. This service must handle secure witness generation and proof computation off-chain. Finally, establish a key management system for your trusted setup ceremony artifacts (the ptau and zkey files), ensuring they are stored securely and access is controlled.

To extend this system, consider integrating more complex compliance logic. Circuits can be designed to prove adherence to Travel Rule requirements, OFAC sanction screenings, or MiCA transaction thresholds. You could also explore recursive proofs to aggregate multiple daily reports into a single weekly or monthly proof, reducing on-chain verification costs. Libraries like zk-email or zk-kit offer pre-built circuits for common primitives that can accelerate development.

The primary challenges in scaling this approach are proving time and cost. Generating a zk-SNARK proof for large datasets can be computationally intensive. As a next step, benchmark your prover performance and explore alternative proving systems like zk-STARKs (using frameworks like StarkWare's Cairo) or PLONK for different performance trade-offs. Keeping the circuit constraint count low is essential for maintaining feasible gas costs on the verifier contract.

For further learning, engage with the open-source ecosystem. Study the circuit code for real-world applications like Tornado Cash (for anonymity) or Dark Forest (for partial information games). Participate in trusted setup ceremonies for public goods like the Perpetual Powers of Tau. The ZKProof Community Standards and documentation for Circom and snarkjs are essential references for staying current with best practices and security considerations in this rapidly evolving field.