Definition ∞ Scalable ZK-ML refers to the ability to execute Machine Learning computations using Zero-Knowledge proofs in a way that efficiently handles large datasets and complex models. This technology allows for the verification of computation correctness without revealing the underlying data or model parameters. It addresses the challenge of applying privacy-preserving machine learning at a practical scale.
Context ∞ The intersection of Zero-Knowledge proofs and Machine Learning, known as ZK-ML, is a frontier in privacy-preserving artificial intelligence, with Scalable ZK-ML being a key objective. Discussions center on optimizing cryptographic circuits and proof generation processes to reduce computational overhead for real-world ML applications. A critical future development involves the deployment of Scalable ZK-ML in decentralized finance and other sensitive data environments, enabling verifiable and private computations at an industrial scale.
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