Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
Authors: Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments |
| Researcher Affiliation | Collaboration | 1AI in Healthcare and Medicine and Institute of Radiology, Technical University of Munich, Germany 2Mathematical Foundations of AI, LMU Munich 3Google Deep Mind. |
| Pseudocode | Yes | B.3. -Divergence Implementation The following code listing implements the -divergence computation corresponding to the mechanisms in Figure 3 in Python. |
| Open Source Code | No | The paper includes a code listing in Appendix B.3 but does not explicitly state that this code is open-source or publicly released via a repository. |
| Open Datasets | Yes | Concretely, the authors calibrate seven CIFAR-10 training runs with different noise multipliers and numbers of steps while fixing the sampling rate to obtain models which all satisfy p8, 10 5q-DP. |
| Dataset Splits | No | The paper references parameters and validation accuracy from De et al. (2022) (e.g., 'validation accuracy of 72.6%') but does not explicitly describe the training/validation/test dataset splits used for its own experiments in the main text. |
| Hardware Specification | No | No specific hardware (GPU, CPU models, etc.) used for running experiments is mentioned in the paper. |
| Software Dependencies | No | The paper lists Python libraries like `scipy.stats` and `numpy` in its pseudocode but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We compare two SGMs M, Ă M with σ 2, rσ 3, p rp 9 10 4, N 1.4 106 and r N 3.4 106. |