提出三项量子安全神经网络设计原则，应对未来量子攻击威胁。
Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities
- 基于后量子密码与抗量子架构构建防御体系
- 提出透明可审计的开发部署机制保障可信性
- 适合关注量子时代机器学习安全的研究者
随着量子计算技术的发展，构建抗量子攻击的神经网络至关重要。本文提出三项量子安全设计原则：(1)采用后量子密码学，(2)使用抗量子神经网络架构，(3)确保开发与部署过程的透明性与可问责性。这些原则依托多种量子策略，包括量子数据匿名化、抗量子神经网络和量子加密。论文还指出了量子安全、隐私与信任方面的开放问题，并建议探索自适应对抗攻击与自动对抗攻击作为未来方向。所提出的指导原则为构建量子时代安全可靠的机器学习模型提供了框架。
原文摘要 · Abstract (English)
As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment. These principles are supported by various quantum strategies, including quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The paper also identifies open issues in quantum security, privacy, and trust, and recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions. The proposed design principles and recommendations provide guidance for developing quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era.
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