Launching a Prediction Market with On-Chain Privacy Rollups

Prediction markets allow users to bet on the outcome of future events, from election results to sports scores. Traditional on-chain markets, built on public blockchains like Ethereum, expose all user activity—including bets, positions, and profits—to public scrutiny. This transparency can deter participation due to privacy concerns and front-running risks. On-chain privacy rollups solve this by executing transactions off-chain and submitting only validity proofs to the mainnet, concealing sensitive data while maintaining security. This enables markets for sensitive topics where participants require confidentiality.

The core architecture involves a privacy-focused rollup stack like Aztec Network or a zk-rollup with privacy features. Users deposit funds into a shielded pool on the rollup, where their balance is represented by private notes. A smart contract on the rollup, the market maker, manages the event, odds, and payouts. When a user places a bet, they create a private transaction that updates their note state and the market's liquidity pool without revealing the bet's size or direction on the public ledger. Only the cryptographic proof of a valid state transition is posted to L1.

To implement a basic market, you first need to set up a development environment for your chosen rollup. For Aztec, this involves installing the aztec-nargo compiler and the Aztec Sandbox. The market logic is written in Noir, a privacy-focused domain-specific language. A contract includes functions to: create_market(event_id, resolution_time), place_private_bet(amount, outcome_note), and resolve_market(outcome, disbursement_proof). Settlement uses the rollup's native asset or a private-wrapped stablecoin. All function calls are processed by the rollup's sequencer and proven with zk-SNARKs.

Key design considerations include oracle integration for trustless resolution and liquidity mechanisms. Since the market state is private, you cannot rely on a public price feed. Instead, use an oracle like Chainlink with a data encryption scheme or a committee-based attestation system that submits encrypted results. For liquidity, implement an automated market maker (AMM) curve within the private environment or use a peer-to-peer order book. Managing the privacy set—the group of users whose transactions are batched—is crucial for anonymity; larger batches provide stronger privacy.

Testing and deploying require specialized tools. Use the rollup's local sandbox to simulate private transactions and verify state transitions. Audit the zk-circuit logic for correctness, as bugs can lead to fund loss. For mainnet deployment, you'll interact with the rollup's bridge contract to deploy your compiled circuit. Monitor the market using the rollup's block explorer, which shows proof submissions and contract addresses but not private details. Costs are primarily L1 data availability fees and prover computation; optimizing circuit size is essential for scalability.

Practical use cases extend beyond speculation to corporate forecasting, sensitive research polling, and hedging private risks. By leveraging on-chain privacy rollups, developers can build compliant, secure markets that protect user data. The next evolution involves cross-rollup privacy, allowing bets and liquidity to move seamlessly between different private execution environments. Start experimenting with the Aztec documentation or zkSync's ZK Stack to build your first private prediction contract.

The foundation for building a private prediction market is a privacy-focused rollup. This guide uses Aztec Network as the primary example, a ZK-Rollup with programmable privacy. You'll need a basic understanding of zero-knowledge proofs (ZKPs) and smart contract development. Ensure you have Node.js (v18+) and npm or yarn installed. The Aztec Sandbox, a local development network, is essential for testing. Install it globally via npm install -g @aztec/aztec-sandbox.

Your development workflow will involve two main codebases: the private circuit logic and the public rollup contract. The circuit, written in Noir, defines the private state transitions and proof generation for market operations like placing a bet. The public contract, typically written in Solidity for the L1 or in a language like Yul for the rollup's base layer, manages public funds, dispute resolution, and the settlement of market outcomes. You must be comfortable working across these two paradigms.

Set up your project structure. Initialize a new Noir project for your circuit using nargo new prediction_market_circuit. For the public contract, you can use a standard Hardhat or Foundry project. The Aztec.js library is crucial for connecting these pieces; install it in your project with npm install @aztec/aztec.js. This SDK allows your frontend or backend to create proofs, send private transactions, and interact with the Aztec sequencer.

Funding is a critical prerequisite. You will need testnet ETH for the base layer (e.g., Sepolia) to deploy contracts and pay for L1 settlement costs. Participants in your market will also need private testnet tokens on Aztec to place bets confidentially. You can obtain these from a faucet after generating a private wallet using the Aztec SDK. Understand the fee model: users pay for proof generation (compute) and data availability on the rollup.

Finally, architect your application's data flow. A typical private prediction market has encrypted notes for bets stored off-chain, with only a cryptographic commitment posted on-chain. The market outcome is resolved by an oracle (like Chainlink or a decentralized committee) whose data is fed into the public contract. The circuit logic uses this public outcome to privately calculate each user's winnings, which they can then withdraw without revealing their original position.

A privacy-preserving prediction market architecture separates the core market logic from the confidential execution layer. The core logic, deployed as a public smart contract on a base layer like Ethereum, manages market creation, resolution, and the settlement of public outcomes. This contract holds the canonical state of the market but only processes encrypted data. The privacy layer, built using a rollup like Aztec Network or Polygon Miden, handles all confidential operations: users submit encrypted predictions, stakes are managed in private, and the final, anonymized profit/loss calculations are performed off-chain before a zero-knowledge proof of correct execution is submitted to the main contract.

The user flow begins with a participant depositing funds into a private smart contract on the rollup. To place a bet, they generate a zero-knowledge proof that they have sufficient private balance and submit an encrypted transaction specifying their market position. This data is bundled with others into a rollup block. The rollup's sequencer processes these private transactions, updating the encrypted state, and generates a validity proof (like a zk-SNARK) attesting to the correctness of all state transitions without revealing the underlying data. This proof is then posted to the mainnet contract, which verifies it and finalizes the state root of the rollup.

Key technical components include a private data availability solution. While the rollup proof ensures computation integrity, the encrypted transaction data must be available for dispute resolution and future state reconstruction. Solutions like EigenDA with encryption or Celestia's data availability sampling can be used to store this data off-chain cheaply while guaranteeing its availability. Furthermore, the system requires a trusted relay or oracle to feed the market outcome to the privacy rollup in a confidential manner, ensuring the resolution data is available for private settlement without leaking it prematurely to the public chain.

For developers, implementing this involves writing two sets of contracts. The public contract uses a verifier for the specific zk-SNARK circuit of your rollup. The private contract, written in a language like Noir for Aztec or Miden Assembly for Polygon Miden, contains the business logic for managing private balances and calculating payouts. A critical design pattern is the use of nullifiers to prevent double-spending of private notes; when a user withdraws funds, they reveal a nullifier that is recorded on-chain, proving the private note has been spent without revealing which note it was.

This architecture presents trade-offs. The primary benefit is strong privacy for traders, which can reduce front-running and information leakage. However, it introduces complexity in user onboarding (managing zk-proof keys), higher latency due to proof generation, and reliance on the security of the underlying rollup's cryptographic assumptions. For high-throughput markets, the cost of proof generation and verification on L1 becomes the main scalability bottleneck, making the choice of an efficient proof system like PLONK or STARKs crucial.

A privacy rollup for a prediction market consists of two primary off-chain components: the Sequencer and the Prover. The Sequencer is responsible for ordering user transactions, batching them, and submitting compressed data to the base layer (L1). The Prover generates zero-knowledge proofs (ZKPs) that cryptographically verify the correctness of the state transitions within the rollup's private environment, such as bet placements and market resolutions, without revealing underlying data. This separation of duties is critical for scalability and privacy.

To set up the Sequencer, you typically run a node that connects to both the base chain (e.g., Ethereum) and your rollup's peer-to-peer network. You'll need to configure it with the correct chain ID, RPC endpoints, and a funded wallet to pay for L1 data publication. For development, you can use frameworks like Starknet's Madara or zkSync's ZK Stack which provide pre-configured sequencer implementations. The key task is ensuring the Sequencer reliably collects transactions, constructs blocks, and posts the necessary calldata or blobs to the L1.

The Prover setup is more computationally intensive. It requires a machine with a performant CPU (and often a GPU) to generate proofs efficiently. You will install proving software, such as gnark or Halo2, and configure it to connect to your Sequencer or a shared state. The Prover continuously receives batch data, executes the transactions in a shielded context, and generates a SNARK or STARK proof. This proof is then posted on-chain, allowing anyone to verify that the new state root is valid, thereby finalizing the private market outcomes.

Integrating these components with your prediction market application logic is the final step. Your smart contracts on the base layer will hold funds and verify proofs, while your application's backend interfaces with the Sequencer API to submit user transactions. A common pattern is to use a shared mempool where user signed orders are sent, picked up by the Sequencer, processed privately by the Prover, and then settled on-chain. This architecture ensures market data, like individual bet positions, remains confidential throughout the process.

Prediction markets require two core privacy properties: confidential positions and confidential settlement. Traditional on-chain markets expose user bets, allowing front-running and manipulation. Privacy rollups like Aztec or Espresso Systems enable these markets by executing logic within a zero-knowledge proof (ZKP) and posting only encrypted data or commitments to a base layer like Ethereum. This creates a data availability layer for the rollup's state that is publicly verifiable yet reveals no sensitive information about individual trades or outcomes.

The architecture involves a sequencer (or prover) that processes transactions off-chain. For a prediction market on "Will ETH be above $4000 on Jan 1?", users submit private transactions specifying their position (e.g., YES, 10 ETH). The sequencer updates a confidential state tree, where each user's balance and position is encrypted or represented by a cryptographic commitment (like a Pedersen commitment). The sequencer then generates a ZKP (e.g., a zk-SNARK) that proves: all transactions were valid, the new state root is correct, and the market's total liquidity pool balances are consistent, without revealing any individual's data.

Settlement is triggered by an oracle (like Chainlink) posting the final outcome on-chain. The rollup's smart contract verifies the oracle data, then the sequencer submits a final settlement proof. This proof confidentially resolves all positions: it proves that for each commitment in the state tree, the correct payout was calculated based on the oracle result and added to the user's balance, again without revealing who bet what. Users can then withdraw their final balances privately. This ensures the market logic and economic activity are fully enforceable on-chain, while participant data remains hidden.

Implementing this requires a privacy rollup SDK. Using the Aztec.nr framework as an example, you'd write a private prediction market contract. Key functions include create_market(description_hash, resolution_time), private_enter_position(amount, side, secret_hash), and resolve_market(oracle_data). The private_enter_position function would use the user's private key to create a note (a private state object) recording their position, which is inserted into the private data tree. The contract's logic ensures a user cannot spend the same funds twice, enforced by the ZKP circuit.

A critical consideration is data availability for dispute resolution. While state is private, the rollup must publish the data needed to reconstruct the state if the sequencer fails. Solutions like EigenDA or Celestia can store the encrypted data blobs or the necessary KZG commitments. This allows new sequencers to rebuild the state and continue operation, ensuring liveness without compromising privacy. The cost of this data availability is a primary factor in transaction fees for your market.

For developers, the workflow is: 1) Choose a privacy rollup stack (Aztec, Espresso). 2) Write the market logic in a ZK-circuits language (Noir, Cairo). 3) Deploy the verifier contract to L1. 4) Run or connect to a sequencer node. 5) Integrate an oracle for resolution. Testing requires a local sandbox to simulate private transactions and proof generation. This architecture enables prediction markets for sensitive topics (e.g., corporate outcomes) or high-frequency trading where strategy confidentiality is paramount, all while leveraging Ethereum's security for final settlement.

Launching a production-ready prediction market with privacy rollups requires integrating several key systems. Your core stack includes the market logic smart contracts (e.g., on Arbitrum or Base), a ZK-SNARK circuit (using frameworks like Noir or Circom) to prove correct vote encryption, and a sequencer/relayer to batch and submit proofs to L1. The user flow involves depositing funds, submitting encrypted predictions, and later revealing them to claim rewards, with all sensitive data kept off the public chain.

For next steps, begin with a testnet deployment. Use a devnet for your chosen rollup stack (like a local Aztec or Polygon Miden instance) to prototype the privacy circuit and contract interaction. Key development tasks include finalizing your market resolution oracle, stress-testing the relayer's fee economics, and implementing a frontend SDK for generating and managing user keys. Tools like Hardhat or Foundry are essential for contract testing, while libraries like snarkjs facilitate proof generation and verification.

Consider the ongoing operational requirements. You will need to monitor the data availability layer of your rollup to ensure state commitments are published, manage the upgradability of your circuits for security patches, and establish a governance mechanism for market curation. For further learning, review the documentation for specific ZK rollups like Aztec or Polygon zkEVM, and study existing private voting implementations such as MACI.