Differentially Private and Fair Classification via Calibrated Functional Mechanism
Authors: Jiahao Ding, Xinyue Zhang, Xiaohuan Li, Junyi Wang, Rong Yu, Miao Pan622-629
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms. Using real-world datasets, we show that the performance of PDFC and ADFC significantly outperforms the baseline algorithms while jointly providing differential privacy and fairness. Finally, we give the numerical experiments based on real-world datasets and draw conclusion remarks. |
| Researcher Affiliation | Academia | Jiahao Ding,1 Xinyue Zhang,1 Xiaohuan Li,2 Junyi Wang,2 Rong Yu,3 Miao Pan1 1University of Houston 2Guilin University of Electronic Technology 3Guangdong University of Technology |
| Pseudocode | Yes | Algorithm 1 Purely DP and Fair Classification (PDFC) [...] Algorithm 2 Relaxed Functional Mechanism [...] Algorithm 3 Approximately DP and Fair Classification (ADFC) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the performance on two datasets, Adult dataset and US dataset. The Adult dataset from UCI Machine Learning Repository (Dheeru and Karra Taniskidou 2017) [...] The US dataset is from Integrated Public Use Microdata Series (Center 2018) |
| Dataset Splits | Yes | We consider a random 80-20 training-testing split and conduct 10 independent runs of algorithms. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks, or solvers with version numbers). |
| Experiment Setup | Yes | For the parameters of differential privacy, we consider ϵ = {10 2, 10 1.5, 10 1, 100, 100.5, 101}, and δ = {10 3, 10 4, 10 5, 10 6, 10 7}. |