Differentially Private Learning with Small Public Data
Authors: Jun Wang, Zhi-Hua Zhou6219-6226
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the performance of PPSGD and compare it to the following baselines: |
| Researcher Affiliation | Academia | Jun Wang, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {wangj, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 PPSGD |
| Open Source Code | Yes | Detailed experimental setups, results, and Matlab codes for PPSGD can be found at http://www.lamda.nju.edu.cn/code PPSGD.ashx. |
| Open Datasets | Yes | Table 1: Characteristics of real-world datasets. Classification dataset # Sample # Feature % Positive adult-a 32561 123 24.1 ipums-br 38000 52 50.6 ipums-us 39928 57 51.3 magic04 19020 10 64.8 mini-boo-ne 130064 50 28.1 skin 245057 3 20.8 Regression dataset # Sample # Feature Variance cadata 20640 8 0.23 stability 10000 12 0.15 |
| Dataset Splits | No | The paper mentions 80 percent of samples are randomly selected for training and the rest for testing, but does not explicitly describe a separate validation set split. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud resources) are mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab codes' for PPSGD but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | To sum up, we set φ = 10, α = β = 0.3, ϕ = 100 for hinge loss, ϕ = 5 for square loss, and λ {0.01, 0.1, 1}. |