Encrypted Image Classification with Low Memory Footprint Using Fully Homomorphic Encryption.

Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography.

On Spiking Neural Membrane Systems with Neuron and Synapse Creation.

Spiking neural membrane systems are models of computation inspired by the natural functioning of the brain using the concepts of neurons and synapses, and represent a way of building computational systems of a biological inspiration. A variant of such a model, allowing to create new neurons and synapses during the computation, has been considered in the literature to attack computationally hard problems, like problems in the class NP. In this work, we investigate the computational properties of this variant, by proposing three solutions to computationally hard problems, by models with different features, and comparing them with those present in the literature.