Model security refers to the protective measures applied to machine learning models to guard against adversarial attacks and data manipulation. In the context of digital assets, this ensures the integrity and reliability of AI systems used for tasks such as fraud detection, price prediction, or risk assessment. It involves techniques to prevent unauthorized access, tampering, or the exploitation of vulnerabilities within the model’s design or training data. Robust model security is essential for maintaining trust in automated decision-making processes within financial technology.
Context
A key discussion point for model security involves the development of verifiable and auditable AI systems, especially in decentralized applications where transparency is paramount. The challenge lies in securing models against novel adversarial methods while preserving computational efficiency. Future advancements will likely concentrate on zero-knowledge proofs for model inference and federated learning approaches to enhance data privacy and system resilience.
This paper provides the first comprehensive categorization of Zero-Knowledge Machine Learning (ZKML), offering a critical framework to advance privacy-preserving AI and model integrity.
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