Distributed One-class Learning

TL;DR

This paper introduces a privacy-preserving, distributed one-class learning filter for blocking the upload of sensitive images on social media, trained on edge devices without exposing user data or parameters.

Contribution

It presents a novel distributed autoencoder-based filter that ensures user privacy, handles complex data distributions, and resists adversarial attacks in image privacy filtering.

Findings

High accuracy in blocking sensitive images

Effective with increasing number of user classes

Robust against adversarial attempts to access private data

Abstract

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.