Wishart Mechanism for Differentially Private Principal Components Analysis
Authors: Wuxuan Jiang, Cong Xie, Zhihua Zhang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this section, we are going to conduct theoretical analysis of Algorithms 1 and 2 under the framework of differential private matrix publishing. The theoretical support has two parts: privacy and utility guarantee. The former is the essential requirement for privacy-preserving algorithms and the latter tells how well the algorithm works against a nonprivate version. |
| Researcher Affiliation | Academia | Wuxuan Jiang, Cong Xie and Zhihua Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Laplace input perturbation |
| 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 | No | The paper is theoretical and does not mention using or providing access to any dataset for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups involving dataset splits like training, validation, or testing. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |