  Machine Learning as a Service (MLaaS) operators provide model training and
prediction on the cloud. MLaaS applications often rely on centralised
collection and aggregation of user data, which could lead to significant
privacy concerns when dealing with sensitive personal data. To address this
problem, we propose PrivEdge, a technique for privacy-preserving MLaaS that
safeguards the privacy of users who provide their data for training, as well as
users who use the prediction service. With PrivEdge, each user independently
uses their private data to locally train a one-class reconstructive adversarial
network that succinctly represents their training data. As sending the model
parameters to the service provider in the clear would reveal private
information, PrivEdge secret-shares the parameters among two non-colluding
MLaaS providers, to then provide cryptographically private prediction services
through secure multi-party computation techniques. We quantify the benefits of
PrivEdge and compare its performance with state-of-the-art centralised
architectures on three privacy-sensitive image-based tasks: individual
identification, writer identification, and handwritten letter recognition.
Experimental results show that PrivEdge has high precision and recall in
preserving privacy, as well as in distinguishing between private and
non-private images. Moreover, we show the robustness of PrivEdge to image
compression and biased training data. The source code is available at
https://github.com/smartcameras/PrivEdge.
