Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914681508069376 |
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| author | Odoh, Kenneth |
| author_facet | Odoh, Kenneth |
| contents | 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 \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 ($ε$, $δ$)-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10423 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty Odoh, Kenneth Cryptography and Security Artificial Intelligence 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 \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 ($ε$, $δ$)-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. |
| title | Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2402.10423 |