Keyed Chaotic Masking: A Functional Privacy Framework for Neural Inference

This work introduces a lightweight framework for privacy-preserving neural network inference based on keyed chaotic masking a deterministic, user-specific obfuscation method derived from cryptographically seeded chaotic dynamical systems. The approach applies masks to input and output tensors using key-conditioned graph dynamics, enabling authenticated inference, user attribution, and soft output watermarking without modifying model architectures. While the underlying chaotic system used to generate each mask is not analytically invertible, the masking operation itself is algebraically reversible by authorized key holders, offering functional privacy without formal cryptographic guarantees. Unlike traditional encryption or secure multi-party computation, this method operates in continuous space and imposes minimal computational overhead. We describe the construction of the masking system, including graph sampling, dynamical rule selection, and chaos diagnostics. Applications include privacy-preserving inference, secure data contribution, and per-user watermarking in shared model pipelines. This framework offers a practical and modular building block for user-controlled privacy in modern AI systems.