  We propose a cloud-based filter trained to block third parties from uploading
privacy-sensitive images of others to online social media. The proposed filter
uses Distributed One-Class Learning, which decomposes the cloud-based filter
into multiple one-class classifiers. Each one-class classifier captures the
properties of a class of privacy-sensitive images with an autoencoder. The
multi-class filter is then reconstructed by combining the parameters of the
one-class autoencoders. The training takes place on edge devices (e.g.
smartphones) and therefore users do not need to upload their private and/or
sensitive images to the cloud. A major advantage of the proposed filter over
existing distributed learning approaches is that users cannot access, even
indirectly, the parameters of other users. Moreover, the filter can cope with
the imbalanced and complex distribution of the image content and the
independent probability of addition of new users. We evaluate the performance
of the proposed distributed filter using the exemplar task of blocking a user
from sharing privacy-sensitive images of other users. In particular, we
validate the behavior of the proposed multi-class filter with
non-privacy-sensitive images, the accuracy when the number of classes
increases, and the robustness to attacks when an adversary user has access to
privacy-sensitive images of other users.
