  We detail a new framework for privacy preserving deep learning and discuss
its assets. The framework puts a premium on ownership and secure processing of
data and introduces a valuable representation based on chains of commands and
tensors. This abstraction allows one to implement complex privacy preserving
constructs such as Federated Learning, Secure Multiparty Computation, and
Differential Privacy while still exposing a familiar deep learning API to the
end-user. We report early results on the Boston Housing and Pima Indian
Diabetes datasets. While the privacy features apart from Differential Privacy
do not impact the prediction accuracy, the current implementation of the
framework introduces a significant overhead in performance, which will be
addressed at a later stage of the development. We believe this work is an
important milestone introducing the first reliable, general framework for
privacy preserving deep learning.
