ProofSight Litepaper

Executive Summary

1. The Transparency Paradox in Prediction Markets

1.1 Current Market Limitations

Traditional prediction markets, both centralized and decentralized, suffer from inherent design flaws that limit their effectiveness as information aggregation mechanisms:

1.2 The Need for Privacy

Financial privacy is not merely a preference—it is an economic necessity. In traditional finance, dark pools facilitate over 40% of U.S. equity trading volume precisely because institutional traders require privacy to execute large orders without market impact. Prediction markets, as mechanisms for information aggregation and risk transfer, require similar privacy guarantees to attract sophisticated participants.

ProofSight addresses this critical gap by introducing cryptographic privacy to prediction markets while maintaining the transparency necessary for trustless settlement and fair market operations.

2. ProofSight Architecture

2.1 System Overview

ProofSight's architecture is built on three fundamental layers that work in concert to provide privacy-preserving market operations:

2.2 Transaction Flow

3. Cryptographic Foundations

3.1 Zero-Knowledge Proof System

ProofSight employs SNARKs (Succinct Non-Interactive Arguments of Knowledge) as the foundational cryptographic primitive. Specifically, we utilize the Groth16 proving system due to its optimal proof size and verification efficiency on Solana's constrained compute environment.

3.2 Hash Functions and Commitment Schemes

We use the Poseidon hash function, specifically designed for zero-knowledge circuits, to construct commitment schemes and Merkle trees. Poseidon offers significant advantages over traditional hash functions like SHA-256 in ZK contexts:

• Reduced constraint counts (up to 10x fewer constraints than Pedersen hashes)
• Native field arithmetic compatibility with modern proof systems
• Proven security against algebraic attacks
• Efficient implementation in both circuit and native code

3.3 Anonymity Set Construction

The security of ProofSight's privacy guarantees depends on the size and composition of the anonymity set—the group of users among whom any individual transaction cannot be distinguished. We implement several mechanisms to maximize anonymity set size:

4. Solana Integration

4.1 Why Solana?

The choice of Solana as the underlying blockchain infrastructure is driven by several critical technical requirements:

4.2 On-Chain Program Architecture

ProofSight's on-chain programs are written in Rust using the Anchor framework, consisting of four primary program modules:

5. Privacy Guarantees and Threat Model

5.1 What ProofSight Hides

5.2 What Remains Public

5.3 Adversarial Considerations

ProofSight's security model assumes the following adversarial capabilities:

• Network-level surveillance: Adversary can monitor all Solana network traffic and transaction patterns
• Timing analysis: Adversary attempts to correlate deposits, positions, and withdrawals based on timing
• Sybil attacks: Adversary creates multiple accounts to fragment the anonymity set
• Side-channel attacks: Adversary attempts to extract information through gas usage patterns or computational timing

Our cryptographic constructions and protocol design specifically address these attack vectors through batched transactions, uniform gas costs, and decoy mechanisms.

6. Economic Model and Fee Structure

6.1 Fee Components

ProofSight implements a sustainable fee structure designed to incentivize liquidity provision while keeping costs competitive for traders:

6.2 Liquidity Provision

Market makers can provide liquidity to prediction markets and earn fees from trading activity. Liquidity providers (LPs) receive 80% of trading fees, with the remaining 20% allocated to protocol development and security audits.

7. Security Architecture

7.1 Trusted Setup Ceremony

Groth16 proofs require a trusted setup phase to generate proving and verification keys. ProofSight will conduct a multi-party computation (MPC) ceremony involving diverse stakeholders from academia, industry, and the community. The ceremony requires only one honest participant to ensure security.

7.2 Smart Contract Security

7.3 Oracle Security

Market outcome resolution relies on oracle feeds. ProofSight integrates with multiple oracle providers including Chainlink, Pyth Network, and Switchboard, with dispute resolution mechanisms for edge cases.

8. Use Cases and Market Applications

9. Development Roadmap

10. Future Research and Open Problems

ProofSight represents the first generation of privacy-preserving prediction markets. Several areas of ongoing research will shape the protocol's evolution:

11. Conclusion

ProofSight represents a fundamental advancement in prediction market design, solving the transparency paradox that has limited institutional adoption and market efficiency. By leveraging zero-knowledge cryptography and Solana's high-performance infrastructure, we enable a new class of prediction markets where privacy and transparency coexist.

The protocol creates economic incentives for high-signal capital to participate in information aggregation without the strategic risks imposed by full transparency. As prediction markets mature into critical infrastructure for collective intelligence and risk management, privacy-preserving designs like ProofSight will become essential for unlocking their full potential.

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