Distributed One-class Learning
This addresses privacy protection for social media users by preventing unauthorized image sharing, though it appears incremental as it builds on distributed learning approaches with specific enhancements.
The authors tackled the problem of blocking third parties from uploading privacy-sensitive images to social media by proposing a cloud-based filter using Distributed One-Class Learning, which decomposes the filter into multiple one-class classifiers trained on edge devices without uploading private images, and achieved validation in tasks like handling non-privacy-sensitive images and robustness against attacks.
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.