Nowadays, Machine learning is employed in many different everyday problems, especially in the form of Neural Networks. However, the raising computational load and the heavy resource requirements of these structures have led to solutions based on cloud-based services. Moreover, to improve the efficiency and the power consumption of the models’ calculations, it has been introduced a new generation of neural networks called Spiking Neural Network which aims to overcome the previous limitations by imitating the human brain behavior. Its sparse, dynamic and eventdriven analytic capabilities improve meaningfully the performances reducing overall costs. In the last few years, however, what has been considered concerning about this type of service is related to the privacy preservation of the confidential data evaluated by the networks held by servers.