Ensuring Data Privacy in Deep Learning through Compressive Learning

Recent years have seen the wide application of deep learning algorithms in a collaborative setting where multiple participants contribute to the training process of the algorithm. For example, users may submit images to be used collectively to train a machine learning model for image classification. A major concern of collaborative learning is protecting privacy of the participants; this could refer to concealing either their identity or the data they provide. In many cases, we want to make sure that data cannot be directly associated with a specific individual when the model or updates to the model are shared.

In this proposal, we will develop new methods to learn deep learning models with differential privacy (DP) guarantee using compressive learning approaches. We will work with a dataset that concerns AI-assisted video content moderation, in which deep-learning based segmentation and classification models have been used to identify explicit image content in the videos and to suggest appropriate levels (moderate, severe etc) for such content.

The proposed research avoid the drawbacks of current approaches as well as achieve lower computational cost and be applicable in more general data and analysis scenarios. it will thus remove current computational barriers of applying private deep learning for AI-assisted video content moderation at scale. Being able to guarantee that the content of individual training images are private will help minimize the risk that content is leaked and thus that younger age groups are exposed to unsuitable leaked content.