Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty
<|reference_start|>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 ($\epsilon$, $\delta$)-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.<|reference_end|>
@article{odoh2024connect,
    title={Connect the dots: Dataset Condensation, Differential Privacy, and
  Adversarial Uncertainty},
    author={Kenneth Odoh},
    journal={arXiv preprint arXiv:2402.10423},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10423},
    primaryClass={cs.CR cs.AI}
}