Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference

• URL: http://arxiv.org/abs/2505.23655v3
• Date: Tue, 03 Jun 2025 16:59:29 GMT
• Title: Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference
• Authors: Peter David Fagan,
• Abstract summary: This work introduces a novel encryption method for ensuring the security of neural inference.<n>By constructing key-conditioned chaotic graph dynamical systems, we enable the encryption and decryption of real-valued tensors within the neural architecture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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