Adversarial Learning of Privacy-Preserving and Task-Oriented Representations

Understand

Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems.

• For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks.
• Our work aims at learning a privacy-preserving and task-oriented representation to defend against such model inversion attacks.
• Specifically, we propose an adversarial reconstruction learning framework that prevents the latent representations decoded into original input data.