Quantization-Aware Optimization for Cryptographic Neural Network Inference

Abstract

Cryptographic neural network inference seeks to preserve the confidentiality of inputs, models, and outputs while still allowing useful predictions to be computed across trust boundaries. The practical difficulty is that secure computation protocols impose substantial communication, memory, and latency costs, especially when neural networks contain nonlinear activation functions and high-precision parameters. Quantization-aware optimization has emerged as a critical systems strategy because it changes both the arithmetic intensity and the protocol structure of private inference. This paper examines quantization-aware optimization as a socio-technical architecture problem rather than a narrow numerical compression technique. It analyzes how reduced precision interacts with secure multiparty computation, homomorphic encryption, trusted execution, and hybrid deployment models. The discussion emphasizes structural trade-offs among accuracy, privacy, latency, energy use, robustness, fairness, and governance. It argues that quantization can reduce cryptographic overhead by simplifying nonlinear operations, lowering communication volume, and enabling integer-arithmetic execution, yet it can also introduce approximation vulnerabilities, distributional bias, and auditing difficulties. The paper considers infrastructure implications for cloud, edge, and regulated environments, as well as lifecycle sustainability concerns. It concludes that quantization-aware cryptographic inference requires co-design across machine learning, cryptography, hardware, policy, and organizational governance. Without integrated evaluation, optimization may improve benchmark performance while weakening accountability, equity, and long-term maintainability.