用混沌图动态系统加密神经网络推理数据，保护隐私
Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference
- 基于密钥控制的混沌图动力系统实现张量加密
- 对初始条件敏感，可生成复杂非线性变换，安全性强
- 适合关注神经网络隐私保护的研究者
神经网络推理通常在原始输入数据上进行，增加了预处理和推理过程中的泄露风险。此外，现有神经架构缺乏直接验证输入数据的有效机制。本文提出一种新型加密方法，通过构建密钥相关的混沌图动力系统，在神经架构中实现实值张量的加密与解密。该动力系统因对初始条件高度敏感且能从简洁规则中生成复杂、依赖密钥的非线性变换，特别适合用于加密。本工作建立了一种保障神经推理安全的新范式，并为图动力系统在神经网络安全中的应用开辟了新方向。
原文摘要 · Abstract (English)
Neural network inference typically operates on raw input data, increasing the risk of exposure during preprocessing and inference. Moreover, neural architectures lack efficient built-in mechanisms for directly authenticating input data. This work introduces a novel encryption method for ensuring the security of neural inference. By constructing key-conditioned chaotic graph dynamical systems, we enable the encryption and decryption of real-valued tensors within the neural architecture. The proposed dynamical systems are particularly suited to encryption due to their sensitivity to initial conditions and their capacity to produce complex, key-dependent nonlinear transformations from compact rules. This work establishes a paradigm for securing neural inference and opens new avenues for research on the application of graph dynamical systems in neural network security.
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