Leveraging Public Data for Practical Private Query Release
Authors: Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Steven Wu
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a theoretical analysis and an empirical evaluation on the American Community Survey (ACS) and ADULT datasets, which shows that our method outperforms stateof-the-art methods. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University, Pittsburgh, PA, USA 2University of Minnesota, Minneapolis, MN, USA 3Google, Mountain View, CA, USA 4Northeastern University, Boston, MA, USA. |
| Pseudocode | Yes | Algorithm 1 PMWPub |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | American Community Survey (ACS). We evaluate all algorithms on the 2018 American Community Survey (ACS), obtained from the IPUMS USA database (Ruggles et al., 2020)... ADULT. We evaluate algorithms on the ADULT dataset from the UCI machine learning dataset repository (Dua & Graff, 2017). |
| Dataset Splits | No | On the ACS dataset, we select hyperparameters for PMWPub using 5-run averages on the corresponding validation sets (treated as private) derived from the 2014 ACS release. |
| Hardware Specification | Yes | Experiments are conducted using a single core on an i5-4690K CPU (3.50GHz) machine. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific libraries). |
| Experiment Setup | Yes | A list of hyperparameters is listed in Table 2 in the appendix. |