  Our work focuses on understanding the underpinning mechanism of dataset
condensation by drawing connections with ($\epsilon$, $\delta$)-differential
privacy where the optimal noise, $\epsilon$, is chosen by adversarial
uncertainty \cite{Grining2017}. We can answer the question about the inner
workings of the dataset condensation procedure. Previous work \cite{dong2022}
proved the link between dataset condensation (DC) and ($\epsilon$,
$\delta$)-differential privacy. However, it is unclear from existing works on
ablating DC to obtain a lower-bound estimate of $\epsilon$ 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,
$\epsilon$. 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.
