Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty

Our work focuses on understanding the underpinning mechanism of dataset condensation by drawing connections with (ε, δ)-differential privacy where the optimal noise, ε, is chosen by adversarial uncertainty citeGrining2017. We can answer the question about the inner workings of the dataset condensation procedure. Previous work citedong2022 proved the link between dataset condensation (DC) and (ε, δ)-differential privacy. However, it is unclear from existing works on ablating DC to obtain a lower-bound estimate of ε that will suffice for creating high-fidelity synthetic data. We suggest that adversarial uncertainty is the most appropriate method to achieve an optimal noise level, ε. As part of the internal dynamics of dataset condensation, we adopt a satisfactory scheme for noise estimation that guarantees high-fidelity data while providing privacy.