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Neural Network Security

Definition ∞ Neural network security refers to the methods and practices applied to protect artificial neural networks from various forms of attack and vulnerabilities. This includes defending against adversarial examples, data poisoning, and model inversion attacks that can compromise the integrity or confidentiality of an AI system. It ensures the reliability and trustworthiness of AI models in critical applications. The goal is to maintain the network’s performance and prevent malicious manipulation.
Context ∞ As AI systems become more prevalent in digital asset analytics, fraud detection, and automated trading, neural network security gains increasing relevance. News may report on new techniques to protect AI models used for market prediction or anomaly detection from being misled by manipulated data. The discussion involves securing these sophisticated computational tools against attacks that could impact financial markets or user holdings.

This macro view showcases a sophisticated piece of hardware, likely a specialized chip or module, rendered in metallic blue and silver tones. Its intricate design features raised metallic textures and recessed ports, suggesting complex data pathways and secure data enclave functionalities. The sharp, geometric lines and the dark, reflective screen-like surface evoke concepts of secure digital asset management and the physical infrastructure underpinning decentralized ledger technologies, such as private key storage or node hardware. It represents the tangible intersection of cryptography and distributed systems.

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GuardianMPC: Backdoor-Resilient Neural Network Computation via Secure MPC

A novel framework leverages secure multi-party computation to protect neural networks from backdoor attacks, ensuring private, robust AI inference and training.