Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference
This addresses privacy concerns for users of neural networks by securing inference processes, though it appears incremental as it builds on existing chaotic dynamics concepts.
The paper tackles the security risks in neural network inference by introducing a novel encryption method using key-conditioned chaotic graph dynamical systems, enabling encryption and decryption of real-valued tensors within neural architectures.
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.