  Deep Neural Network (DNN) Inference in Edge Computing, often called Edge
Intelligence, requires solutions to insure that sensitive data confidentiality
and intellectual property are not revealed in the process. Privacy-preserving
Edge Intelligence is only emerging, despite the growing prevalence of Edge
Computing as a context of Machine-Learning-as-a-Service. Solutions are yet to
be applied, and possibly adapted, to state-of-the-art DNNs. This position paper
provides an original assessment of the compatibility of existing techniques for
privacy-preserving DNN Inference with the characteristics of an Edge Computing
setup, highlighting the appropriateness of secret sharing in this context. We
then address the future role of model compression methods in the research
towards secret sharing on DNNs with state-of-the-art performance.
