How to Implement ZK-Proofs for Private Credential Sharing

Private credential verification solves a core privacy problem: proving you are eligible for a service without exposing sensitive personal data. In traditional systems, sharing a credential like a government ID or university diploma requires handing over the entire document. Zero-knowledge proofs allow a user to generate a cryptographic proof that they hold a valid credential meeting specific criteria, such as being over 18 or holding a degree, while keeping the actual data private. This is foundational for building privacy-preserving identity, access control, and compliance systems on-chain.

The implementation typically involves three core components. First, a trusted issuer (like a university or KYC provider) cryptographically signs a credential, creating a verifiable credential (VC). Second, the credential holder uses a ZK proving system (like Circom or SnarkJS) to generate a proof that their signed VC contains attributes satisfying a public statement. Third, a verifier (a smart contract or server) checks the proof against the public statement and the issuer's public key, validating the claim without seeing the underlying data.

For developers, a common stack uses Circom for writing the arithmetic circuits that define the proof logic and SnarkJS for proof generation and verification. A simple circuit might check that a committed value (like a birth date) is within a valid range. The credential data is kept off-chain, while only the proof and public inputs are sent on-chain. Libraries like @semaphore-protocol/proof and @zk-kit/protocols offer higher-level abstractions for identity-specific ZK proofs, streamlining integration.

Consider a real use case: a decentralized exchange requiring proof of residency without collecting addresses. A user could obtain a verifiable credential from a postal service. To access the DEX, they would generate a ZK-proof demonstrating the credential is valid and contains a country code matching an allowed jurisdiction. The DEX's smart contract verifies the proof in a single transaction. This pattern is used by protocols like Semaphore for anonymous signaling and Polygon ID for private access to DeFi.

Key security considerations include ensuring the issuer's signing key is secure and trusted, and carefully designing the circuit to avoid information leakage. The public statement must be crafted so that verifying the proof cannot inadvertently reveal the secret. For production systems, audit the ZK circuit logic and use battle-tested libraries. The computational cost of proof generation (prover time) and verification (gas cost on-chain) are also critical performance metrics to evaluate.

Implementing ZK-proofs for credential verification requires a solid grasp of core cryptographic concepts and modern development tools. You should be comfortable with public-key cryptography, hash functions, and the fundamental premise of a zero-knowledge proof: proving knowledge of a secret without revealing it. Familiarity with circuit-based ZKPs (like those used by Circom and Halo2) is essential, as credentials are typically verified by proving a statement about private inputs within an arithmetic circuit. A working knowledge of a statically-typed language like TypeScript or Rust is also necessary for interacting with ZKP libraries and smart contracts.

The primary tool for this tutorial is Circom 2, a domain-specific language for defining arithmetic circuits, paired with snarkjs for proof generation and verification. You will need Node.js (v18+) installed. Begin by setting up your project: npm init -y and then install the required packages: npm install circom2 snarkjs. For on-chain verification, you'll need a development environment for Ethereum, such as Hardhat or Foundry, to compile and deploy the verifier smart contracts that snarkjs can generate. These contracts are small, specialized programs that cryptographically confirm a proof is valid.

Your development workflow will involve three distinct phases. First, you design a Circom circuit (credential.circom) that encodes the logic of your credential check—for example, proving a user's age is over 18 without disclosing their birth date. Second, you use snarkjs to perform a trusted setup, compile the circuit, and generate the proving/verification keys. Finally, you integrate the proving key into a client-side application to generate proofs, and the verification key into a smart contract to validate them on-chain. This setup ensures the credential logic is enforced trustlessly by the blockchain's consensus.

A critical and often overlooked step is the trusted setup ceremony (Phase 1 Powers of Tau). For production systems, contributing to or using a secure multi-party computation (MPC) ceremony is non-negotiable for security. For development and testing, snarkjs can generate a temporary, insecure setup. Always remember: the security of your ZK system depends entirely on the secrecy of the toxic waste generated during this setup phase. We will use a pre-generated Powers of Tau file for this tutorial, but you must understand the implications before moving to mainnet.

Zero-knowledge proofs (ZKPs) enable a user, the prover, to convince a verifier that a statement about their private data is true without revealing the data itself. For credentials, this means proving you possess a valid driver's license, university degree, or KYC attestation while keeping the document number, birth date, or issuing authority confidential. This shifts the trust model from sharing raw data to sharing cryptographic proofs, drastically reducing privacy risk and data leakage in applications like decentralized identity (DID), job applications, and age-gated services.

The implementation typically involves three core components: a circuit, a trusted setup, and a proving system. First, you define the logical statement to be proven as an arithmetic circuit using a domain-specific language like Circom or ZoKrates. For a credential, this circuit could check that a hidden birth date is over 18, a hidden signature is valid for a known issuer public key, or a hidden credential hash exists in a Merkle tree of valid credentials. This circuit is compiled into a set of constraints understood by ZK-SNARK or ZK-STARK proving systems.

A critical step for many ZK-SNARKs is the trusted setup ceremony, which generates a proving key and a verification key. For production, use a secure multi-party computation (MPC) ceremony like the one for Tornado Cash or Semaphore to minimize trust. Once setup is complete, the user's client-side software uses the proving key, their private witness data (the actual credential), and public inputs (like the current date) to generate a compact proof. This proof, often just a few hundred bytes, is what is sent to the verifier.

On the verification side, the process is lightweight. The verifier, which could be a smart contract or a server, uses the pre-generated verification key and the public inputs to check the proof's validity. A Solidity verifier contract, for instance, would expose a function like verifyCredentialOver18(bytes calldata proof, uint256 currentDate) that returns a boolean. This allows for permissionless verification on-chain without exposing any user data, enabling private DeFi compliance or DAO voting rights checks.

For developers, libraries like SnarkJS (for Circom) or the Arkworks ecosystem provide the tooling to implement this flow. A basic workflow is: 1) Write and compile your circuit (.circom), 2) Run a trusted setup to generate proving_key.zkey and verification_key.json, 3) Use a client SDK to generate proofs from user witnesses, and 4) Integrate the verifier contract or function into your application. Always audit your circuit logic, as bugs can lead to false proofs.

Real-world systems are building on these concepts. Polygon ID uses ZKPs for reusable identity claims, and Sismo uses them for non-transferable attestations (badges). The key takeaway is that ZKPs transform credential verification from a data-sharing problem into a cryptographic computation problem, enabling privacy-preserving trust. Start by defining the minimal statement you need to prove and choose a proving system (SNARKs for succinctness, STARKS for post-quantum security) that fits your application's trust and performance requirements.

A zero-knowledge credential circuit is a program that defines a set of constraints over private inputs (your credentials) and public inputs (the statement to be proven). Using a zk-SNARK framework like Circom or a zkVM like RISC Zero, you encode the rules for a valid credential. For example, to prove you are over 18 without revealing your birthdate, the circuit would take your private birthdate and a public current_date, then output true only if current_date - birthdate >= 18 years. The proof demonstrates you performed this calculation correctly, without leaking the birthdate value.

The circuit must be designed with deterministic logic and fixed structure. All possible execution paths and constraints must be defined at compile time. You cannot have dynamic loops or branches that depend on secret values. Common operations include arithmetic comparisons, hash functions (like Poseidon or SHA-256), and signature verifications. For instance, proving membership in a Merkle tree requires the circuit to verify a hash chain from your credential to a public root, a standard pattern for anonymous attestations.

Here is a simplified Circom 2.0 template for a credential age check circuit:

circomCopy
pragma circom 2.0.0;
template AgeCheck() {
    // Private input: the user's birthdate (as a timestamp)
    signal input birthdate;
    // Public input: the current date and the required age
    signal input current_date;
    signal input required_age_years;
    
    // Public output: 1 if valid, 0 if not
    signal output isValid;
    
    // Calculate age in seconds
    signal age_in_seconds <- current_date - birthdate;
    // Convert required age to seconds (approx 31536000 sec/year)
    signal required_seconds <- required_age_years * 31536000;
    
    // Constraint: age must be >= required age
    component comparator = GreaterEqThan(32);
    comparator.in[0] <== age_in_seconds;
    comparator.in[1] <== required_seconds;
    
    isValid <== comparator.out;
}

This circuit ensures the core relationship holds, but a production system would need more robust time handling and input signal management.

Key design considerations include circuit size and proving time. Each constraint adds to the computational cost. Using optimal primitives is critical; a Poseidon hash requires far fewer constraints than SHA-256 within a zk-SNARK. Furthermore, you must decide what becomes a public signal. Exposing too much (e.g., the specific date of verification) can leak information. The goal is to minimize public inputs to only what the verifier absolutely needs to know.

Finally, the circuit must be audited for logical soundness. A bug in the constraint logic could allow false proofs. Common pitfalls include overflows in arithmetic, incorrect bit lengths for signals, and timezone handling in date logic. The compiled circuit (the .r1cs file in Circom) becomes a trusted setup artifact, so its correctness is paramount before proceeding to proof generation and verification.

A Circom circuit defines the constraints of a computation without revealing the inputs. For private credential sharing, the circuit's primary job is to prove you possess a valid credential (like a password hash or a signed attestation) that matches a public commitment, without disclosing the credential itself. The circuit takes private inputs (your secret), public inputs (the commitment stored on-chain), and outputs a single public signal, typically 1, to indicate a successful verification. All logic is expressed using arithmetic operations over a finite field.

Start by defining the main component, VerifyCredential, which will be your circuit's entry point. You must declare its input and output signals using the signal keyword. For a simple hash-based credential, you would have a private input secret, a public input commitment, and a public output verified. The core constraint uses a template, like the Poseidon hash function from the circomlib library, to compute the hash of the secret and enforce that it equals the public commitment.

circomCopy
include "circomlib/poseidon.circom";

template VerifyCredential() {
    signal input secret;
    signal input commitment;
    signal output verified;

    component hash = Poseidon(1);
    hash.inputs[0] <== secret;

    commitment === hash.out;
    verified <== 1;
}

component main {public [commitment]} = VerifyCredential();

The === operator creates a constraint; the proof is only valid if the hash output equals the provided commitment. The public [commitment] declaration in the main component is crucial—it tells the prover and verifier which values are known to both parties. For more complex credentials, such as proving membership in a Merkle tree or verifying a cryptographic signature, you would integrate additional templates from circomlib like MerkleTreeInclusionProof or EdDSASignatureVerification and chain their constraints together.

After writing the circuit, you must compile it using the Circom compiler (circom circuit.circom --r1cs --wasm --sym). This generates the R1CS (Rank-1 Constraint System) file, which represents the circuit in a format usable by snarkjs, and a WASM module for witness generation. Always test your circuit with sample inputs using a script to generate a witness and ensure it produces the expected output (verified = 1) for valid credentials and fails for invalid ones, confirming your constraints are correct before proceeding to proof generation.

The trusted setup is a foundational ceremony that generates the public parameters (a proving key and a verification key) needed for your ZK-SNARK circuit. For private credentials, this setup is typically performed using a Perpetual Powers of Tau ceremony, like the one from the Semaphore team, which provides a universal and reusable reference string. You download this .ptau file, then use it with your compiled circuit to create the final provingKey.zkey and verificationKey.json files. This step is 'trusted' because if the ceremony participants are compromised, proofs could be forged, making decentralized multi-party ceremonies (MPCs) the security standard.

With the proving key ready, you can now generate a ZK proof. This process uses a witness—the private inputs that satisfy the circuit's constraints without revealing them. For a credential proof, the witness includes the secret credential (like a private key or password hash) and any required public signals (like a public commitment). Using libraries like snarkjs or circomlib, you compute the witness and then run the proving algorithm. The output is a compact proof (often just a few hundred bytes) and the public outputs of the circuit, which are submitted for verification.

Here is a practical example using snarkjs in Node.js after circuit compilation and setup:

javascriptCopy
const { witness, publicSignals } = await snarkjs.wtns.calculate({
  // Private inputs (witness)
  in: secretCredential,
  // Public inputs
  in_public: publicCommitment
}, compiledCircuit.wasm, 'witness.wtns');

const { proof, publicSignals } = await snarkjs.groth16.prove(
  'provingKey.zkey',
  'witness.wtns'
);

This proof object, combined with the publicSignals, is the final ZK-proof that can be verified on-chain or off-chain, demonstrating knowledge of the credential without exposing it.

Proof generation is computationally intensive, often requiring WebAssembly runtimes or backend servers. For browser-based applications, consider generating proofs in a Web Worker to avoid blocking the main thread. The choice of proving system matters: Groth16 proofs are small and cheap to verify on-chain but require a circuit-specific trusted setup. PLONK or Halo2 schemes offer universal setups but may have larger proof sizes. Benchmark your circuit to choose the optimal balance for your application's latency and cost requirements.

Finally, the verification key produced during setup is used to check the proof's validity. On Ethereum, this involves deploying a verifier smart contract (generated from the verificationKey.json) that uses precompiles like ECADD and ECPAIRING. The contract's verifyProof function accepts the proof and public signals, returning a boolean. This on-chain verification, costing ~200k-500k gas for Groth16, is the trust anchor that allows a dApp to grant access based solely on a valid proof of credential ownership.

The core of a private credential system is the separation of proof generation (off-chain) and proof verification (on-chain). Off-chain, a user's client—often a browser extension or mobile wallet—uses a ZK-SNARK or ZK-STARK proving system to generate a proof. This proof cryptographically demonstrates that the user possesses credentials satisfying specific rules (e.g., "I am over 18" or "My credit score is >700") without revealing the underlying data. Libraries like Circom or Halo2 are used to write the arithmetic circuits that define these rules, which are then compiled into prover and verifier keys.

For off-chain implementation, you typically work with a circuit written in a domain-specific language. Using the Circom example, after defining your circuit (credentialVerifier.circom), you compile it to generate the prover key (proving_key.zkey) and verifier key (verification_key.json). The user's client then uses a library like snarkjs to generate the proof by providing the witness (their private inputs that satisfy the circuit) and the proving key. The output is a small, succinct proof (e.g., a few hundred bytes) and any necessary public signals, which are sent to the verifier.

On-chain verification is performed by a verifier smart contract. This contract contains the verification key logic, often as a set of elliptic curve pairing operations. When it receives the proof and public signals, it executes a fixed verifyProof() function. Popular frameworks like SnarkJS and Circom can automatically generate Solidity or Cairo verifier contracts. The contract's sole job is to return true if the proof is valid, enabling conditional logic like granting access to a token-gated Discord server or executing a loan in a DeFi protocol without exposing the user's salary.

A critical design choice is deciding what is made public. The public signals are the non-secret inputs to and outputs from your ZK circuit that are revealed on-chain. For a credential check, this might only be the verifier's address and a nullifier to prevent proof replay. The actual credential data remains private. This setup ensures the blockchain acts as a trustless, transparent judge of the proof's validity while maintaining user privacy, a pattern used by protocols like Semaphore for anonymous signaling or zkBob for private transfers.

To implement this flow, follow these steps: 1) Define your credential logic as a ZK circuit. 2) Compile the circuit and perform a trusted setup or use a Perpetual Powers of Tau ceremony for universal setups. 3) Generate the prover and verifier keys. 4) Integrate proof generation into your client application. 5) Deploy the generated verifier contract to your target chain (Ethereum, Polygon zkEVM, Starknet, etc.). 6) Have your main application contract call the verifier contract to check proofs before proceeding. This architecture decouples complex proof generation from the efficient, on-chain verification.