From LUT to Layer: Neural Network Inference with CKKS Functional Bootstrapping - Saldanha, Matheus de O.; Horn, Guilherme A.; Ferreira, Mauricio L.; Purim, Andreis G. M.; Cassinelli, Alex L. D.; Pereira, Hilder V. L. (Anais do Simpósio Brasileiro de Cibersegurança (SBSeg), 2026)
Neural networks implemented with homomorphic cryptography usually rely on costly polynomial approximations for non-linear activation functions. This work presents, to the best of our knowledge, the first implementation of neural-network inference using the recently proposed CKKS functional bootstrapping framework for arbitrary Look-Up Tables (LUTs). We implement fully connected neural networks for MNIST classification using LUT-based functional bootstrapping of sign, Heaviside, and ReLU activations, are compare against the traditional Chebyshev polynomial approximation. Across all evaluated configurations, the functional pipeline reduces inference latency by approximately 2 to 3.5 times while preserving accuracy more effectively for discontinuous activations at larger hidden widths, at the cost of an average memory overhead of approximately 1.6 times.