Setting Up a Zero-Knowledge Proof System for Anonymous Peer Review

Anonymous peer review is critical for unbiased feedback in academia and open-source development, but traditional systems often lack cryptographic guarantees of anonymity and can be gamed. Zero-knowledge proofs (ZKPs) offer a solution: a reviewer can prove they performed a valid review—checking for specific criteria or meeting a reputation threshold—without revealing who they are. This tutorial explores setting up a basic ZKP-based system using Circom for circuit design and SnarkJS for proof generation, creating a trustless framework where review quality is verifiable but reviewer identity remains hidden.

The core of the system is a ZK circuit that encodes the review logic. For example, a circuit can verify that a reviewer has a minimum reputation score (from an on-chain registry) and that their review contains a substantive analysis, perhaps by checking a hash of the review text against a predefined format. Using Circom, you define these constraints. A simple circuit might confirm a reviewer's public key is in a Merkle tree of eligible reviewers and that they signed the review. The prover (reviewer) generates a proof attesting to these facts without disclosing their specific leaf in the tree.

Here is a simplified Circom template for verifying a Merkle tree inclusion proof, a common primitive for anonymous credential systems. This circuit proves the reviewer knows a secret that commits to a valid leaf in the tree of authorized reviewers.

circomCopy
include "circomlib/circuits/poseidon.circom";
include "circomlib/circuits/merkleTree.circom";

template AnonymousReviewerCheck(depth) {
    signal input root;
    signal input secret; // Reviewer's private identity
    signal input pathElements[depth];
    signal input pathIndices[depth];
    
    component poseidon = Poseidon(1);
    poseidon.inputs[0] <== secret;
    signal leaf <== poseidon.out;
    
    component mt = MerkleTreeChecker(depth);
    mt.leaf <== leaf;
    mt.root <== root;
    for (var i = 0; i < depth; i++) {
        mt.pathElements[i] <== pathElements[i];
        mt.pathIndices[i] <== pathIndices[i];
    }
    mt.out === 1;
}

After compiling this circuit with circom, you use SnarkJS to setup a trusted ceremony, generate proving/verification keys, and create proofs. The resulting proof and public signals (like the Merkle root) are submitted to a verifier contract.

The verification typically happens on-chain via a smart contract. A Solidity verifier contract, generated by SnarkJS, checks the ZK proof against the public inputs (e.g., the root of the reviewer set and a hash of the review). If valid, the contract can mint a soulbound NFT or record a hash on-chain as immutable evidence of a completed review. This creates a transparent, auditable record of review activity where the content and contributor are cryptographically verified, but the link between the reviewer's on-chain identity and their real-world self is broken.

Key considerations for production systems include trusted setup management for your ZK circuit, ensuring the eligibility Merkle tree is updated securely, and designing incentive mechanisms. Projects like Semaphore offer pre-built frameworks for anonymous signaling. Potential applications extend beyond academia to DAO governance, where voters prove membership and voting power anonymously, or bug bounty programs, where white-hat hackers can prove they found a vulnerability without exposing their identity until a reward is secured.

Building a zero-knowledge proof (ZKP) system for anonymous peer review requires a foundational stack of cryptographic tools and development environments. You will need a working knowledge of a systems programming language like Rust or C++, as most high-performance ZKP libraries are built in these languages. Essential tools include a package manager (e.g., Cargo for Rust), a code editor like VS Code, and Git for version control. The core of your setup will be a ZKP framework; popular choices include Arkworks for its modularity in Rust or libsnark for C++, which provide the necessary primitives for constructing and verifying proofs.

The next prerequisite is understanding the cryptographic backend. Your system will rely on elliptic curve cryptography and pairing-friendly curves. For most modern zk-SNARK constructions, you will need to select a curve such as BN254 (Barreto-Naehrig) or BLS12-381. Libraries like ark-ec and ark-ff within Arkworks abstract these complexities. You must also install a constraint system compiler. For a Rust-based workflow, this is often circom (written in Rust), which compiles high-level circuit logic into the rank-1 constraint system (R1CS) format that proof systems like Groth16 require. Install it via cargo install circom.

Finally, you need to set up the proving and verification key infrastructure. This involves a trusted setup ceremony to generate the Common Reference String (CRS), a critical step for systems like Groth16. For development and testing, you can use a toxic waste ceremony with a randomly generated secret, but a production system requires a secure multi-party computation (MPC) setup. Tools like the powersoftau library facilitate this process. Your initial project structure should separate circuit definitions, proof generation logic, and verification contracts, ensuring a clean architecture for the anonymous review protocol you will build on top of these ZKP primitives.

A ZKP-based anonymous peer review system allows reviewers to cryptographically prove they have performed a valid review of a submission without revealing their identity or the content of their evaluation. The architecture typically involves four main actors: the Author submitting work, the Reviewer providing feedback, a Coordinator (often a smart contract) managing the process, and a Verifier (anyone) who can check proof validity. The core cryptographic primitive is a zk-SNARK or zk-STARK circuit that encodes the review rules.

The system workflow begins with an off-chain setup. The Coordinator defines the review criteria (e.g., "score must be between 1 and 5") and a trusted party generates the proving and verification keys for the ZKP circuit using a ceremony like Groth16 or PLONK. The Author submits their work, encrypted or hashed, to a public ledger or storage layer like IPFS, receiving a commitment. Reviewers then privately assess the work and generate a ZKP attestation that their review satisfies all rules, submitting only the proof and the output (e.g., a passing grade) to the Coordinator.

On-chain, the Coordinator's smart contract, deployed on a chain like Ethereum or a ZK-rollup, holds the verification key. It receives the proof and public outputs from reviewers. Using precompiles or libraries like snarkjs or circom, the contract runs the verify() function. If valid, it accepts the review and can aggregate results or trigger a payout, all without learning the reviewer's identity or specific scores. This ensures selective disclosure: the system only learns what is absolutely necessary.

Key technical challenges include designing a circuit that is expressive enough for complex review logic yet efficient to prove, and managing the trust assumptions in the setup phase. Using universal and updatable reference strings, as in the Perpetual Powers of Tau ceremony, can mitigate trust issues. The architecture must also handle data availability—ensuring the reviewed content is accessible for verification—potentially using decentralized storage solutions with content-addressed hashes referenced on-chain.

For implementation, developers can use frameworks like Circom for circuit writing and SnarkJS for proof generation in JavaScript, or Halo2 (used by Zcash) for more complex logic in Rust. An example circuit would take private inputs (reviewer identity, raw score) and public inputs (submission hash, minimum score). It would output a boolean isValid and a public commitment to the reviewer, proving the score is within bounds without revealing it. The entire system's state and verification logic can be anchored to a blockchain for tamper-resistance and auditability.

An arithmetic circuit is a computational model that expresses a verification program as a series of constraints over a finite field. For credential verification, the circuit's public inputs are the nullifier (a unique identifier for the review) and the merkle root of the approved reviewer list. The private inputs are the user's credential (e.g., a secret token or key) and the merkle proof that validates their credential against the published root. The circuit's job is to prove, in zero-knowledge, that these private inputs satisfy the public verification logic.

We implement this using a ZK-SNARK framework like Circom or Halo2. The circuit must perform two primary checks. First, it hashes the private credential to generate a commitment. Second, it verifies that this commitment is a valid leaf within the Merkle tree defined by the public root, using the supplied merkle path and sibling hashes. A successful proof demonstrates the user possesses a valid credential from the authorized set, without disclosing which specific one.

Here is a simplified Circom template for the core verification logic. This circuit, CredentialVerifier, takes the private witness data and public signals, then outputs a valid signal (1 or 0).

circomCopy
pragma circom 2.0.0;
include "circomlib/poseidon.circom";
include "circomlib/merkleTree.circom";

template CredentialVerifier(levels) {
    // Public Inputs
    signal input root;
    signal input nullifier;
    // Private Inputs
    signal input secret;
    signal input pathElements[levels];
    signal input pathIndices[levels];
    // Output
    signal output valid;

    // Hash the secret credential to create the leaf
    component hash = Poseidon(1);
    hash.inputs[0] <== secret;
    signal leaf <== hash.out;

    // Verify the leaf is in the Merkle Tree with the given root
    component mtv = MerkleTreeVerifier(levels);
    mtv.leaf <== leaf;
    for (var i = 0; i < levels; i++) {
        mtv.pathElements[i] <== pathElements[i];
        mtv.pathIndices[i] <== pathIndices[i];
    }
    mtv.root <== root;

    // The proof is valid if Merkle verification passes
    valid <== 1;
}

After defining the circuit, you must compile it to generate the proving key and verification key. This is done using the framework's compiler (e.g., circom for Circom). The proving key is used by reviewers to generate proofs, while the verification key is embedded into the on-chain smart contract to validate those proofs. The choice of a secure hash function like Poseidon (common in ZK circuits for its efficiency in SNARKs) and the tree depth (levels) are critical security parameters that affect gas costs and resistance to brute-force attacks.

Finally, this circuit design enables the core property of the system: unlinkability. The same user can submit multiple reviews by generating a unique nullifier for each (e.g., by hashing their secret with a nonce). The circuit verifies the credential each time, but the public signals (nullifier and root) reveal no information linking different reviews back to the same individual, fulfilling the requirement for anonymous peer review.

With your circuit compiled and the proving key generated, you can now create a proof. This proof is a cryptographic object that attests to the correct execution of your circuit on specific private inputs, without revealing those inputs. For our peer review system, the private inputs are the reviewer's identity and their secret score, while the public inputs are the review hash and the minimum score threshold. Using a library like snarkjs, you generate the proof by running the groth16 prove command, which consumes the proving key and the witness file created in the previous step.

The generated proof must be rigorously tested off-chain to ensure correctness and avoid costly on-chain failures. This involves a local verification step using the verification key. A successful verification confirms that the proof is valid for the given public inputs. It's also critical to test edge cases: what happens if the secret score is below the threshold? The proof should fail verification. Testing with different identities and scores validates the circuit's logic for conditions like score >= minScore and the correct computation of the reviewHash = poseidon(reviewerId, score).

For developers, integrating this into a Node.js script automates the process. You would use the snarkjs JavaScript library to programmatically generate the proof from witness data, then immediately call the verify function. This script becomes part of your CI/CD pipeline. Remember, the witness file contains the computed values for all signals in the circuit; its accuracy is paramount. Any mismatch between the expected public outputs (like the review hash you commit to on-chain) and those in the witness will cause verification to fail.

Finally, before considering the proof ready for chain submission, serialize it into the format your chosen blockchain's verifier contract expects. For Ethereum and EVM-compatible chains using the Groth16 verifier, this typically means extracting the proof as three elliptic curve points (A, B, C) and the public inputs as an array of field elements. Tools like snarkjs zkey export soliditycalldata can generate this calldata. Testing with a local fork of the blockchain using Hardhat or Foundry, and calling the verifier contract directly, provides the highest confidence before a mainnet deployment.

The core of the system is the Submission Smart Contract, deployed on a blockchain like Ethereum, Polygon, or a zkEVM chain. This contract acts as the public registry and verification hub. Its primary functions are to: accept encrypted review data, verify attached zero-knowledge proofs, and emit events upon successful verification. The contract does not store the review content, only the proof and a commitment to the data, preserving anonymity.

To submit a review, the reviewer's client (e.g., a web app) must first generate a zk-SNARK or zk-STARK proof. Using a library like Circom or Halo2, you create a circuit that proves the reviewer knows a valid review (e.g., a word count > 50, a score within 1-10) without revealing the text or their identity. The circuit output is a proof and a public hash of the data, known as a commitment. The client then calls the smart contract's submitReview(bytes calldata proof, bytes32 commitment) function.

Inside the submitReview function, the contract performs a critical verification step. It calls a verifier contract, typically generated by your zk toolkit (like snarkjs), which contains the logic to check the proof against the circuit's verification key. A successful verification confirms the review is valid per your predefined rules, without the contract ever seeing the private inputs. The contract then stores the commitment in a public mapping and emits an event, providing an immutable, anonymous record of the submission.

For developers, the integration requires careful management of gas costs and proof size. zk-SNARK proofs are small (~200 bytes) and cheap to verify on-chain, making them ideal. The verifier contract must be pre-deployed. Your front-end needs to handle proof generation, which can be computationally intensive; consider using a WebAssembly (WASM) module in the browser or a backend service for this task. Always use established libraries and audit the circuit logic thoroughly, as bugs here compromise the entire system's integrity.

A practical next step is to build an indexing service or subgraph that listens to the contract's submission events. This creates a queryable database of anonymous review commitments, which can be used to display aggregate statistics—like average scores or submission counts—without ever revealing individual reviewer data. This completes the loop, providing a functional, trust-minimized system for anonymous peer review on the blockchain.

The system you've built demonstrates a practical application of zero-knowledge proofs for privacy-preserving reputation. The core workflow involves: a reviewer generating a ZK-SNARK proof attesting to their qualifications and review completion off-chain, then submitting only that proof and a public output hash to an on-chain verifier contract. This allows the platform to validate that a qualified, non-colluding reviewer completed the work without revealing their identity or the review's contents. Key technologies used include Circom for circuit design, SnarkJS for proof generation, and a Solidity verifier deployed to an EVM-compatible chain like Ethereum or a Layer 2.

For production deployment, several critical next steps are required. First, conduct a formal security audit of your Circom circuits to prevent logic flaws or cryptographic vulnerabilities. Second, implement a robust nullifier system to prevent proof replay attacks, ensuring each review proof can be submitted only once. Third, design a decentralized attestation registry (potentially using Ethereum Attestation Service or Verax) to allow qualified reviewers to obtain anonymous, verifiable credentials that serve as private inputs to your circuit, replacing the simplistic whitelist.

To extend the system's functionality, consider integrating zk-Email or similar protocols to verify reviewer credentials from traditional sources (like institutional emails) in a privacy-preserving manner. You could also explore Semaphore for anonymous signaling or Interep for group reputation. For higher throughput, migrate the verifier contract to a ZK-rollup like zkSync Era or Scroll, where proof verification is native and cost-effective. Always monitor for new zkDSL developments; languages like Noir or updated Circom versions may offer improved developer experience and security.

The final step is to design a fair incentive mechanism. This typically involves a staking and slashing system where reviewers deposit collateral that can be slashed for malicious behavior, with rewards paid out upon successful, verified review submission. The entire proof generation and submission flow should be abstracted into a seamless user experience via a SDK or a dedicated client, hiding the cryptographic complexity from the end-user reviewer while maintaining the protocol's trustless guarantees.