  Deep-learning-as-a-service is a novel and promising computing paradigm aiming
at providing machine/deep learning solutions and mechanisms through Cloud-based
computing infrastructures. Thanks to its ability to remotely execute and train
deep learning models (that typically require high computational loads and
memory occupation), such an approach guarantees high performance, scalability,
and availability. Unfortunately, such an approach requires to send information
to be processed (e.g., signals, images, positions, sounds, videos) to the
Cloud, hence having potentially catastrophic-impacts on the privacy of users.
This paper introduces a novel distributed architecture for
deep-learning-as-a-service that is able to preserve the user sensitive data
while providing Cloud-based machine and deep learning services. The proposed
architecture, which relies on Homomorphic Encryption that is able to perform
operations on encrypted data, has been tailored for Convolutional Neural
Networks (CNNs) in the domain of image analysis and implemented through a
client-server REST-based approach. Experimental results show the effectiveness
of the proposed architecture.
