  We present Syft 0.5, a general-purpose framework that combines a core group
of privacy-enhancing technologies that facilitate a universal set of structured
transparency systems. This framework is demonstrated through the design and
implementation of a novel privacy-preserving inference information flow where
we pass homomorphically encrypted activation signals through a split neural
network for inference. We show that splitting the model further up the
computation chain significantly reduces the computation time of inference and
the payload size of activation signals at the cost of model secrecy. We
evaluate our proposed flow with respect to its provision of the core structural
transparency principles.
