The last decades have seen a significant development of Artificial Neural Networks (ANNs). These advancements have enabled the application of ANNs to various domains, such as handwriting and speech recognition, or classification and computer vision tasks. However, the high power effort required by these NNs has pushed the research toward the low power domain. A new paradigm of NNs named the Spiking Neural Networks (SNNs) has demonstrated promising results in terms of performance and efficiency. Moreover, the success of NNs lies on the great availability of data, which may contain sensitive information. Hence, it is challenging from the privacy perspective to maintain the confidentiality of data.