Zero-Knowledge Machine Learning

Definition ∞ Zero-knowledge machine learning is a field that combines machine learning with zero-knowledge proofs. This allows machine learning models to make predictions or perform computations on sensitive data without revealing the data itself. The model can prove that it has performed a computation correctly, or that a certain condition is met, without disclosing any proprietary or private information used in the process. This technology is pivotal for privacy-preserving AI applications.
Context ∞ The application of zero-knowledge machine learning is gaining traction in sectors requiring high levels of data privacy, including digital asset management and decentralized identity solutions. Current research focuses on optimizing the performance of zero-knowledge ML inference and training processes, which can be computationally intensive. Debates are ongoing regarding the practical scalability of these systems and the development of standardized frameworks for their secure deployment in sensitive applications.

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→Proof Accumulation

→Model Transparency

→Verifiable AI

ZKTorch: Efficiently Verifying ML Inference with Zero-Knowledge Proofs

ZKTorch introduces a parallel proof accumulation system for ML inference, fundamentally enhancing transparency while safeguarding proprietary model weights.

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→Homomorphic Polynomial Commitment

→Decentralized Artificial Intelligence

→Non-Interactive Argument of Knowledge

Artemis SNARKs Efficiently Verify Cryptographic Commitments for Decentralized Machine Learning

Artemis, a new Commit-and-Prove SNARK, drastically cuts the commitment verification bottleneck, enabling practical, trustless zero-knowledge machine learning.

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→Succinct Arguments

→Prover Efficiency

→Trusted Setup

Black-Box Commit-and-Prove SNARKs Unlock Verifiable Computation Scaling

Artemis, a new black-box SNARK construction, modularly solves the commitment verification bottleneck, enabling practical, large-scale zero-knowledge machine learning.

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→Black-Box SNARK Integration

→Commit and Prove SNARKs

→Proof System Generality

Commit-and-Prove SNARKs Generalize Verifiable Computation for Machine Learning

A new Commit-and-Prove primitive enables efficient, black-box integration of homomorphic commitments into any SNARK, unlocking scalable verifiable AI.

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→Verifiable Inference

→Lookup Arguments

→Cryptographic Proof Systems

Zero-Knowledge Proofs Verifiably Secure Large Language Model Inference

A novel ZKP system, zkLLM, enables the efficient, private verification of 13-billion-parameter LLM outputs, securing AI integrity and intellectual property.

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→Meritocratic Consensus

→Swarm Inference

→Distributed Intelligence

Peer-Ranked Consensus Secures Decentralized AI Swarm Inference.

Research introduces a peer-ranked consensus protocol using on-chain reputation and proof-of-capability to create a meritocratic, Sybil-resistant foundation for verifiable decentralized AI services.

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→Prover Time Efficiency

→Homomorphic Polynomial Commitment

→Zero-Knowledge Machine Learning

Commit-and-Prove SNARKs Enable Efficient Verifiable Machine Learning

A new Commit-and-Prove SNARK architecture decouples witness commitment, achieving succinct verifier time for large, private inputs like ML models.

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→Polynomial Commitment

→Hyrax Scheme

→Sumcheck Protocol

Verifiable Fine-Tuning Secures Large Language Models with Zero-Knowledge Proofs

zkLoRA is a new framework that cryptographically verifies LLM fine-tuning correctness without revealing model weights, unlocking private, auditable AI.

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→Homomorphic Polynomial Commitment

→SNARK Arithmetization

→zkML Efficiency

Black-Box Commit-and-Prove SNARKs Accelerate Verifiable Machine Learning Efficiency

Artemis introduces a black-box Commit-and-Prove SNARK architecture, radically cutting prover time by decoupling commitment checks from the core verifiable computation.

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→Zero-Knowledge Machine Learning

→Trustless Setup

→Proof System Modularity

Artemis CP-SNARKs Enable Practical, Verifiable, Privacy-Preserving Machine Learning

Artemis CP-SNARK is a modular construction that eliminates the commitment verification bottleneck in zkML, making large-scale, privacy-preserving AI models practical.