October 2, 2025|zkVerify

Revolutionizing Privacy-Preserving Machine Learning with ZK Proof Verification

In the intersection of machine learning (ML) and blockchain, privacy remains a critical barrier to widespread adoption. ShadowML, a decentralized platform for confidential ML predictions, is addressing this by enabling model providers to offer services without exposing proprietary details. Born from zkVerify's ZK Online Hackathon for Web3 Builders with Arbitrum & EduChain, ShadowML has evolved into a partner now sending ZK proofs to zkVerify—a specialized blockchain for efficient proof verification from Horizen Labs. This collaboration allows ShadowML to verify predictions on-chain securely, fostering a marketplace for privacy-first ML while reducing costs and enhancing trust.

What is ShadowML and Why Does It Matter?

ShadowML is a platform that empowers ML model providers to monetize their models through per-request fees while keeping the models, training datasets, and proprietary thresholds private. Developed by team Ctrl-Alt-Elite, it uses zero-knowledge proofs to deliver verifiable predictions without revealing sensitive information, starting with a proof-of-concept based on the Iris dataset for flower classification via a decision tree model. This approach tackles the challenge of balancing functionality with privacy in ML, where traditional systems often require full disclosure, risking intellectual property theft or data breaches.

In a world where ML drives decisions in high-stakes fields, ShadowML's privacy-preserving mechanism is vital. It supports a decentralized marketplace where users pay for predictions, and providers earn without compromise, making it scalable for complex models and datasets in the future.

The zkVerify Integration: Streamlining Verification for ML Proofs

zkVerify functions as a modular layer optimized for ZK proof verification across schemes, cutting costs by up to 90% compared to general-purpose chains. ShadowML, having participated in zkVerify's hackathon, now partners by sending generated proofs for on-chain verification via zkVerify's network. This integration uses RISC Zero zkVM for proof generation in Rust, a Node.js verification server with REST APIs, and smart contracts like ZkMLMarketplace.sol for merkle tree validation and event emission.

The setup involves environment configurations for zkVerify RPC and contracts, allowing seamless proof submission and attestation. A React frontend handles user interactions, including proof generation and real-time status tracking, all backed by zkVerify's efficient settlement.

The Use Case: Proving Correct ML Predictions Without Exposure

ShadowML leverages ZK proofs to attest that ML predictions are accurate and derived from the specified model without disclosing the model itself, input data, or intermediate computations. Specifically, the proofs validate:

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Prediction Integrity: That the output (e.g., Iris flower type based on sepal/petal measurements) matches the model's execution.
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Model Execution: Correct running of the decision tree or similar logic on private datasets.
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Confidential Inputs/Outputs: Assertions about results without revealing proprietary thresholds or data points.
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Marketplace Transactions: Verifiable per-request payments and attestations for successful predictions.

Proofs are generated via endpoints like /generate-proof, then verified through /verify on zkVerify, emitting events for confirmation. This is ideal for scenarios where ML insights are needed but privacy is non-negotiable, extending from simple classifications to advanced diagnostics.

The Value to Users: Privacy, Monetization, and Trust in ML

For ML providers, users, and industries, this ZK-enabled partnership delivers key advantages:

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Robust Privacy Protection: Models and data stay confidential, reducing risks in sensitive sectors like healthcare (e.g., patient diagnostics) and finance (e.g., credit scoring).
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Monetization Opportunities: Providers earn fees per prediction, creating a sustainable marketplace without IP exposure.
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Efficiency and Scalability: zkVerify's low-cost verification makes frequent proofs feasible, supporting growth to complex models.
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Enhanced Trust and Compliance: Users verify predictions independently, ensuring accuracy and regulatory adherence (e.g., data protection laws).
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Accessibility: No need for users to build models; they access high-quality predictions via a user-friendly interface, with video tutorials for guidance.

Ultimately, it empowers a privacy-first ML ecosystem, bridging Web3 and AI for secure, decentralized intelligence.

Looking Ahead: The Future of ZK in Machine Learning Marketplaces

The ShadowML-zkVerify tie-up, sparked at the hackathon, positions zkVerify as a catalyst for ZK innovations in ML. With plans to expand model diversity and industry applications, ShadowML could transform how AI is deployed in Web3. Developers can dive into the GitHub repo or hackathon resources to build similar solutions.