  Machine learning (ML) is widely used today, especially through deep neural
networks (DNNs), however, increasing computational load and resource
requirements have led to cloud-based solutions. To address this problem, a new
generation of networks called Spiking Neural Networks (SNN) has emerged, which
mimic the behavior of the human brain to improve efficiency and reduce energy
consumption. These networks often process large amounts of sensitive
information, such as confidential data, and thus privacy issues arise.
Homomorphic encryption (HE) offers a solution, allowing calculations to be
performed on encrypted data without decrypting it. This research compares
traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption
scheme. The LeNet-5 model, a widely-used convolutional architecture, is used
for both DNN and SNN models based on the LeNet-5 architecture, and the networks
are trained and compared using the FashionMNIST dataset. The results show that
SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the
plaintext modulus t, although their execution time is longer due to their
time-coding nature with multiple time-steps.
