Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases
Authors: Xiyang Liu, Sewoong Oh
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments supporting our theoretical predictions in Section 3. Figure 1 (a) illustrates the Mean Square Error (MSE) for estimating dĪµ(P%Q) between uniform distribution P and Zipf distribution Q... We demonstrate how we can use Algorithm 2 to detect mechanisms with false claim of DP guarantees on four types of mechanisms |
| Researcher Affiliation | Academia | Xiyang Liu Sewoong Oh Allen School of Computer Science and Engineering, University of Washington EMAIL |
| Pseudocode | Yes | Algorithm 1 Differential Privacy (DP) estimator with known P ... Algorithm 2 Differential Privacy (DP) estimator |
| Open Source Code | Yes | We present the experiment details in Appendix B and the code to reproduce our experiments at https://github.com/xiyangl3/adp-estimator. |
| Open Datasets | No | The paper mentions using 'uniform distribution P and Zipf distribution Q' (synthetic data) and 'real-world experiments' but does not provide any concrete access information (link, DOI, citation with author/year) for these or any other publicly available datasets. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, such as percentages, absolute counts, or references to predefined splits for model training. |
| Hardware Specification | Yes | All experiments were performed on a machine with Intel Core i7-4770K CPU (3.50GHz) and 32GB RAM. |
| Software Dependencies | No | The paper mentions 'Python 3.6' with a version but does not provide version numbers for other key software components like 'numpy', thus not fulfilling the requirement of multiple versioned components or all mentioned components being versioned. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings. It refers to Appendix B.2 for settings, but this appendix discusses the mechanisms rather than detailed training configurations. |