How to Architect a Decentralized KYC/AML Framework for Prediction Markets

Prediction markets, which allow users to speculate on future events, face significant regulatory hurdles. Centralized platforms often implement Know Your Customer (KYC) and Anti-Money Laundering (AML) checks by collecting and storing sensitive personal data, creating central points of failure and privacy risks. A decentralized framework aims to shift this paradigm. Instead of a single entity holding all data, the goal is to create a system where a user proves their eligibility to participate—such as being of legal age and not on a sanctions list—without revealing their full identity to the market operator or other users.

The core architectural challenge is verifying real-world identity attributes on-chain without exposing the underlying data. This is achieved through zero-knowledge proofs (ZKPs) and decentralized identifiers (DIDs). A user first completes KYC with a trusted, licensed Identity Verifier (IV). The IV does not store the data but issues a verifiable credential (VC), a cryptographically signed attestation. This credential contains claims like "over 18" or "not sanctioned," which the user can then use to generate a ZKP. The proof cryptographically demonstrates the user meets the required criteria without revealing their name, date of birth, or passport number.

For the prediction market smart contract, the architecture involves two key components: a Registry and a Verification Module. The Registry is an on-chain, permissioned list of trusted public keys from accredited Identity Verifiers. The Verification Module is the contract logic that accepts a user's ZKP and the corresponding VC, verifies the VC's signature against the trusted Registry, and then validates the ZKP itself. If both checks pass, the contract mints a non-transferable Access Token (an ERC-721 or Soulbound Token) to the user's address. This token serves as their persistent, reusable pass to enter the prediction market's liquidity pools or place bets.

A practical implementation must handle revocation and expiry. Credentials can be revoked if a user's status changes (e.g., they are added to a sanctions list). This is typically managed via a revocation registry, where the IV publishes cryptographic nullifiers. The smart contract's Verification Module must check this registry during each transaction. Furthermore, credentials should have expiration dates to ensure periodic re-verification. The architecture must also consider gas costs; generating ZKPs off-chain and submitting only the succinct proof for on-chain verification is essential for usability. Libraries like Circom or SnarkJS are commonly used for proof generation.

Finally, this architecture enables composability and privacy across DeFi. A user's Access Token from one compliant application could be recognized by another, creating a portable reputation layer. However, key trade-offs exist. The system's security depends on the trustworthiness of the selected Identity Verifiers in the Registry. Furthermore, while user privacy is enhanced from the perspective of the dApp, the IV still sees the raw KYC data, requiring robust data governance. This model represents a middle ground—offering stronger privacy than centralized KYC while providing a verifiable compliance audit trail for regulators, which is crucial for the long-term adoption of prediction markets.

Before architecting a decentralized KYC/AML framework, you must understand the core components of a prediction market. A prediction market is a platform where users trade shares in the outcome of future events, requiring a settlement layer (like a smart contract on Ethereum or Polygon), an oracle service (such as Chainlink or Pyth) to resolve events, and a liquidity mechanism. The KYC/AML layer must integrate with these components without compromising the market's censorship-resistance or creating a single point of failure. Start by reviewing the Augur v2 or Polymarket whitepapers to understand market mechanics.

Technical prerequisites include proficiency with Zero-Knowledge Proof (ZKP) cryptography, which is essential for privacy-preserving compliance. Frameworks like zk-SNARKs (via Circom and SnarkJS) or zk-STARKs are used to prove identity attestations without revealing underlying data. You'll also need experience with decentralized identity standards, primarily the W3C Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) specifications. Implementations like Veramo or SpruceID's Kepler provide SDKs for managing these credentials. A working knowledge of IPFS or Arweave for credential storage and The Graph for indexing attestation events is also recommended.

From a regulatory and system design perspective, you must map jurisdictional requirements. The Financial Action Task Force (FATF) Travel Rule and the EU's Markets in Crypto-Assets (MiCA) regulation define obligations for Virtual Asset Service Providers (VASPs). Your architecture must support risk-based approaches, sanction screening, and audit trails. System requirements include a secure enclave or trusted execution environment (TEE) for processing sensitive PII, a relayer network to pay gas fees for users (meta-transactions), and a modular design separating the credential issuer, verifier, and revoker roles. Tools like Oasis Protocol's Parcel or Secret Network can handle confidential computation.

A decentralized KYC/AML framework for prediction markets must balance regulatory compliance with core Web3 principles: user privacy, censorship resistance, and self-custody. The architecture is not a single smart contract but a modular system of off-chain verifiers, on-chain registries, and zero-knowledge proofs. The primary goal is to prove a user is verified—without revealing their identity on-chain—before they can place high-value bets or access permissioned markets. This prevents the blockchain from becoming a public ledger of personal data while enabling market operators to meet jurisdictional requirements.

The data flow begins when a user submits identity documents to a trusted off-chain verifier. This could be a regulated entity, a decentralized oracle network like Chainlink, or a specialized protocol like Fractal ID. The verifier performs the KYC/AML checks, which include sanction list screening and liveness detection. Upon successful verification, the verifier issues a cryptographic attestation. Crucially, this attestation is not the raw data but a signed proof, often in the form of a verifiable credential (W3C VC) or a zero-knowledge proof, that the user has passed the required checks.

This attestation is then used to generate a privacy-preserving on-chain token. One common implementation is a soulbound token (SBT) or a non-transferable NFT placed in the user's wallet. A more advanced method uses zk-SNARKs, where the user generates a proof that they hold a valid credential from a trusted issuer. The user then submits this proof to a gatekeeper contract on the prediction market. The contract verifies the proof's validity and the issuer's signature, then grants the user an access credential, such as a role in an OpenZeppelin AccessControl contract, allowing them to interact with restricted functions.

The core smart contract architecture involves several key components. A Registry Contract maintains a whitelist of accredited KYC provider addresses. A Verification Gateway is the main entry point; it calls the registry to check an issuer's status and verifies the cryptographic proof submitted by the user. A Compliance Oracle can be used for ongoing monitoring, periodically checking if a user's credential has been revoked (e.g., via a revocation registry or a real-time oracle update) and triggering a function to deactivate their access.

For developers, implementing the gateway contract involves verifying ECDSA signatures or zk-SNARK proofs. A basic signature verification function in Solidity might look like this:

solidityCopy
function verifyKYC(address user, bytes memory signature, address issuer) public view returns (bool) {
    bytes32 messageHash = keccak256(abi.encodePacked(user, "KYC_APPROVED"));
    bytes32 ethSignedMessageHash = keccak256(abi.encodePacked("\x19Ethereum Signed Message:\n32", messageHash));
    return issuer == ecrecover(ethSignedMessageHash, v, r, s);
}

This function checks that a trusted issuer signed a message attesting to the user's status.

The final consideration is data minimization and user sovereignty. The architecture should support multiple verification tiers (e.g., basic for small bets, enhanced for large limits) and allow users to reuse credentials across different prediction markets via standard formats like Verifiable Credentials. The system's success depends on its modularity, allowing markets to choose their compliance level and verifiers, creating a competitive ecosystem for trust services that doesn't force a single point of failure or surveillance.

The core design principle is a separation of concerns. You should create at least two primary smart contracts: a KYC Registry and a Prediction Market Factory. The KYC Registry is a stateful contract that manages user identities, verification statuses, and compliance tiers (e.g., TIER_0_UNVERIFIED, TIER_1_BASIC_KYC, TIER_2_ENHANCED_DUE_DILIGENCE). The Prediction Market Factory is a separate contract that deploys individual market instances, each checking the KYC Registry before allowing users to participate. This modularity allows the KYC logic to be upgraded independently and reused across multiple market applications.

The KYC Registry contract must implement a permissioned, non-transferable token standard for identity, such as ERC-721 or ERC-1155. Each minted token represents a verified identity slot for a user. Using a non-transferable standard is critical to prevent the sale or laundering of verified identities. The contract's state should store essential, privacy-preserving metadata hashes—like a hash of a user's proof-of-identity document and their assigned compliance tier—rather than storing raw personal data on-chain. Access to update a user's status should be restricted to a set of approved Attesters, which could be governed by a DAO or a multisig wallet.

For the Prediction Market contracts, integration is achieved through a simple interface check. Before a user can create a market, place a bet, or withdraw winnings, the market contract calls a function on the KYC Registry, such as getKycTier(address user). This returns the user's compliance tier. The market logic then enforces rules based on that tier; for example, only users with TIER_2_ENHANCED_DUE_DILIGENCE might be allowed to participate in high-stakes markets with large position limits. This design keeps market contracts simple and agnostic to the specific verification process.

A critical consideration is data minimization and privacy. You should never store Personally Identifiable Information (PII) directly on the public blockchain. Instead, use a commit-reveal scheme or zero-knowledge proofs. A common pattern is for the user to submit a hash of their verified credential (e.g., a keccak256 hash of a passport scan and a secret salt) to the KYC Registry. The corresponding plaintext data and proof are stored off-chain in a decentralized storage solution like IPFS or Ceramic, with the on-chain hash serving as an immutable, privacy-preserving reference.

Finally, you must design for upgradability and governance. The set of Attesters and the rules for each compliance tier will likely need to evolve. Using a proxy pattern (like the Transparent Proxy or UUPS) for the KYC Registry allows you to fix bugs or add features without migrating all user data. Governance over these upgrades and the Attester set can be managed by a token-weighted DAO, ensuring the system remains decentralized and responsive to the community's regulatory and security needs.

A decentralized KYC/AML framework relies on two core W3C standards: Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). A DID is a user-owned, cryptographically verifiable identifier (e.g., did:ethr:0xabc123...) that is not dependent on a central registry. A VC is a tamper-evident credential, like a proof of residency or accredited investor status, issued by a trusted entity (an Issuer) and cryptographically signed. The user holds their DID and VCs in a digital wallet, presenting them on-demand to verifiers like your prediction market dApp.

The architecture follows a triangular trust model. First, a regulated Issuer (like a KYC provider) verifies a user's identity off-chain and issues a signed VC to their wallet. This VC contains claims (e.g., "countryOfResidence": "US") and is linked to the user's DID. Second, the Holder (the user) stores this VC securely. Third, the Verifier (your prediction market smart contract or backend) requests proof. The user creates a Verifiable Presentation—a package of selected VCs—and the verifier cryptographically checks the issuer's signature and the credential's validity status without seeing the underlying personal data.

For on-chain verification, use zero-knowledge proofs (ZKPs) to maintain privacy. Instead of submitting a raw VC stating "age > 18", the user generates a ZK-SNARK or ZK-STARK proof that cryptographically confirms the claim is true without revealing their birth date. Implement this using libraries like Circuits for snarkjs or SDKs from Sismo or iden3. Your market's KYCVerifier.sol contract would have a function verifyZKProof(bytes calldata proof, bytes32 publicSignals) that checks the proof against a trusted verification key. This ensures regulatory compliance without on-chain data leaks.

To manage credential revocation and real-time status, integrate a Verifiable Data Registry. This is often a smart contract acting as a revocation registry, such as an Ethereum Attestation Service (EAS) schema or a Ceramic stream. When an issuer revokes a credential (e.g., for a sanctions update), they post a revocation entry to this registry. Your verifier contract must check this registry status during the verification step. For example, it would resolve the credential's unique identifier and query the EAS contract to ensure the attestation is not marked revoked, adding a critical layer of dynamic compliance.

A practical implementation flow for a market restricting access to non-US users is: 1) User obtains a ProofOfNonUSResidency VC from an issuer. 2) Your dApp requests a Verifiable Presentation. 3) The user's wallet (using Veramo or SpruceID's Kepler) signs a presentation containing a ZK proof derived from that VC. 4) Your off-chain verifier or on-chain contract validates the proof and the credential's revocation status on EAS. 5) Upon success, the contract mints a gatekeeping NFT or sets a mapping address => bool isKYCd to grant market access. This creates a reusable, privacy-preserving compliance check.

Key considerations for production include issuer trustworthiness (audit their DID method and issuance process), credential schema design (define standard fields using json-schema), and gas optimization for on-chain verification. Use established frameworks like Veramo for agent-based management or SpruceID's Sign-In with Ethereum kit for integration. This architecture shifts the compliance burden to specialized issuers while giving users control over their data, fulfilling AML 'Travel Rule' requirements through verifiable, auditable presentations without maintaining a central user database.

The core of a privacy-preserving KYC/AML framework is a zero-knowledge proof (ZKP) system. Instead of submitting a passport scan or national ID to a prediction market's smart contract—which would leak personal data on-chain—a user generates a cryptographic proof. This proof attests to a statement like "I have a verified identity from a trusted provider and am not on any sanctions lists" without revealing the underlying documents or the identity itself. Protocols like zk-SNARKs (e.g., using Circom or Halo2) or zk-STARKs are typically employed for this, allowing for succinct verification on-chain.

Architecturally, this requires an off-chain prover service and an on-chain verifier contract. The user's client interacts with a KYC provider (like Fractal ID or Civic) to obtain attested credentials. These credentials are used as private inputs to generate a ZKP off-chain. The public inputs and the resulting proof are then sent to the verifier contract. The contract checks the proof's validity against a predefined circuit that encodes the compliance rules (e.g., age >= 18, country not in banned_list, sanctions_score == 0). Only the proof validity (true/false) and a nullifier (to prevent reuse) are stored on-chain.

A critical component is the circuit design, which defines the provable statements. For a prediction market, key circuits include: ProofOfUniqueHuman (via proof-of-personhood or government ID), ProofOfJurisdiction (country/residency checks), and ProofOfSanctionsStatus. These circuits can be composed. For instance, using zk-Proof Composition, a user could generate a single proof that satisfies all required AML predicates, minimizing gas costs and complexity for the final on-chain verification.

To prevent Sybil attacks, the system must include a mechanism for nullifier generation. When a user generates a proof, a unique nullifier—derived from a secret and the application context—is computed and published on-chain. The verifier contract maintains a registry of used nullifiers. If the same user tries to generate a second compliant identity for the same application, the nullifier will repeat and the transaction will be rejected, ensuring one-compliance-per-person per market, while preserving anonymity across different dApps.

In practice, developers can leverage existing frameworks to build this layer. Semaphore is a protocol for anonymous signaling, ideal for proving group membership (e.g., "I am a verified user"). ZK-EVMs like zkSync or Polygon zkEVM offer native ZKP tooling for custom circuits. For a production system, the prover logic can be deployed as a serverless function (e.g., AWS Lambda) or integrated into a user's wallet via SnarkJS. The on-chain verifier is typically a lightweight, gas-optimized Solidity or Vyper contract that imports the verification key generated during the trusted setup.

The frontend's primary role is to orchestrate the user's journey through the KYC/AML process while abstracting its underlying complexity. This involves three core components: a wallet connection module (e.g., using Wagmi or Web3Modal), a state management layer to track verification status, and a UI component library for the verification steps. The frontend must interact with the smart contracts deployed in the previous step—primarily the VerificationRegistry and ZKPVerifier—to submit applications, check status, and verify proofs. Initial design should prioritize a clear, step-by-step flow that informs users exactly what data is required and why.

The user journey begins post-wallet connection. The dApp should immediately query the VerificationRegistry to check if the user's address already holds a valid VerificationToken NFT. If not, the UI presents a secure, embedded portal to the chosen KYC provider (like Fractal ID or Persona). Using OAuth or secure iframe embedding, users authenticate with the provider, submit their documents, and complete any liveness checks. The frontend must listen for off-chain events or poll an API endpoint to detect when the provider issues the cryptographic attestation (a signed credential) to the user's wallet.

Upon receiving the attestation, the frontend guides the user to generate a zero-knowledge proof (ZKP). This is a critical UX hurdle. Using a library like snarkjs in conjunction with the circuit-specific WebAssembly (WASM) files and proving key generated earlier, the frontend executes the proof generation client-side. Code snippet for initiating proof generation:

javascriptCopy
const { proof, publicSignals } = await snarkjs.groth16.fullProve(
  { /* private inputs: hashed KYC data, nullifier */ },
  'circuit.wasm',
  'proving_key.zkey'
);

This step requires clear user feedback, as proof generation can take several seconds.

With the ZKP generated, the frontend calls the ZKPVerifier.verifyProof function on-chain, passing the proof and publicSignals. A successful transaction results in the minting of a VerificationToken NFT to the user's address. The UI should then update in real-time, using an event listener (e.g., VerificationRegistry.VerificationTokenMinted) to confirm success and unlock access to restricted features of the prediction market, such as placing high-stake bets or accessing exclusive markets.

For ongoing compliance, the frontend must handle status re-checks and revocation. Integrate a periodic check (e.g., via a cron job or a socket connection to the provider) to monitor the validity of the underlying credential. If a credential is revoked by the issuer, the frontend should gracefully notify the user and restrict functionality until they re-verify. This architecture ensures the dApp remains compliant while maintaining a non-custodial, privacy-preserving user experience, where personal data never touches the application's servers.

Decentralized prediction markets face a critical regulatory challenge: how to comply with Know Your Customer (KYC) and Anti-Money Laundering (AML) obligations without compromising core Web3 principles of privacy and permissionless access. A purely on-chain approach exposes sensitive user data, while a centralized KYC provider creates a single point of failure and censorship. The solution is a hybrid architecture using zero-knowledge proofs (ZKPs) and decentralized identifiers (DIDs). This allows users to prove they are verified and not on a sanctions list without revealing their underlying identity data to the prediction market smart contract or other participants.

The technical stack begins with a user-facing Identity Verifier. This can be a regulated third-party service or a decentralized protocol like Worldcoin or Polygon ID. The verifier cryptographically attests to a user's KYC status, issuing a verifiable credential (VC). This credential contains claims (e.g., "is over 18," "passes AML screening") signed by the verifier's private key. The user stores this VC locally in a digital wallet, such as a MetaMask Snap or a Ceramic data stream, maintaining full custody of their data. The raw PII (Personally Identifiable Information) never touches the prediction market's infrastructure.

When a user wants to interact with a restricted function—like placing a large bet or withdrawing funds—the prediction market's smart contract requires a ZK-SNARK proof. The user's wallet generates this proof off-chain using libraries like circom or snarkjs. The proof demonstrates that the user holds a valid, unrevoked VC from a trusted issuer, and that the VC contains the required claims, without disclosing the VC's contents. The contract, pre-configured with the verifier's public key, verifies the proof's validity. This process is gas-intensive, so optimization and the use of verification key abstraction are crucial.

For ongoing AML compliance, the system must handle credential revocation. If a user's status changes (e.g., they are added to a sanctions list), the verifier updates a revocation registry, such as a Merkle tree or a smart contract. The ZKP must now also prove non-membership in this revocation set. Oracles like Chainlink can feed updated regulatory lists on-chain. The prediction market's governance should manage the list of trusted verifiers and the specific claim requirements, allowing for adaptability across jurisdictions like the EU's MiCA or the US. A modular design lets users choose from multiple compliant verifiers.

Implementing this requires careful smart contract design. The core contract should include functions to verifyZKProof(bytes calldata _proof), check a revocationRegistry, and manage a trustedIssuers mapping. Off-chain, a relayer service may be needed to pay gas fees for users submitting proofs, preserving privacy. This architecture shifts the compliance burden to the edge while enabling prediction markets to operate globally. It demonstrates that regulatory compliance and decentralization are not mutually exclusive, but require innovative cryptographic primitives and thoughtful system design.