Hardware-Accelerated Privacy-Preserving Neural Network Inference via Efficient Activation Design

Abstract

Privacy-preserving neural network inference has shifted from a narrow cryptographic curiosity to a systems problem whose viability depends on hardware, compilers, network conditions, governance, and model architecture. Secure multiparty computation, homomorphic encryption, and trusted execution environments each impose distinct computational and communication bottlenecks, but activation functions consistently emerge as a central point of tension. Nonlinear activations such as the rectified linear unit are essential to modern predictive accuracy, yet they are expensive under encryption and secret sharing because they require comparisons, branching, or high-degree approximation. This paper examines hardware-accelerated privacy-preserving inference through the lens of efficient activation design. It argues that activation choices are not merely model-level hyperparameters but cross-layer infrastructure decisions that shape accelerator utilization, memory movement, communication rounds, energy consumption, auditability, and fairness. The analysis considers graphics processing units, field-programmable gate arrays, application-specific integrated circuits, and heterogeneous cloud-edge deployments. It then explores how ReLU-reduced architectures, quantized activations, lookup tables, polynomial approximations, and mixed-protocol execution alter the trade-offs among latency, throughput, accuracy, privacy guarantees, and sustainability. The paper also discusses governance implications, including certification, liability, export controls, and equitable access. The central claim is that efficient activation design can unlock practical private inference only when it is co-designed with hardware acceleration, cryptographic protocols, and policy frameworks rather than treated as an isolated optimization.