Efficient Homomorphic Evaluation of Weightless Neural Networks for Privacy-Preserving Machine Learning

This work introduces the efficient homomorphic evaluation of Weightless Neural Networks (WNNs), including the Wilkie, Stonham, and Aleksander's Recognition Device (WiSARD), for both training and inference on encrypted data. The proposed framework achieves significant performance improvements over the state-of-the-art on homomorphic evaluation of neural network training, while maintaining high accuracy.