A Study of Improving The Privacy-Utility Trade-off of Task-specific Models with Learnable Privacy

In recent years, machine learning (ML) models have been integrated into various applications and products to improve user experience. However, this approach raises significant concerns about the protection of private user data utilized for training the models. One limitation of vanilla privacy methods is that they can improve the robustness of the models against privacy attacks at the cost of accuracy while performing ML tasks. We propose a framework for implementing privacy models that learn privacy budgets to improve the trade-off between privacy of user data, task models, and their utility (task accuracy). The experimental results show that our framework dramatically enhances the task accuracy of ML models in image classification tasks while providing better privacy protection compared to the state-of-the-art methods.