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].
Principal Component Analysis in the Local Differential Privacy Model
Authors: Di Wang, Jinhui Xu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real world datasets con๏ฌrm the theoretical guarantees of our algorithms. |
| Researcher Affiliation | Academia | Di Wang , Jinhui Xu Department of Computer Science and Engineering State University of New York at Bu๏ฌalo, NY, USA. {dwang45,jinhui}@bu๏ฌalo.edu. |
| Pseudocode | Yes | Algorithm 1 Local Gaussian Mechanism and Algorithm 2 Local Gaussian Mechanism-High Dimension |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or links to a code repository. |
| Open Datasets | Yes | For real world datasets, we run Algorithm 1 on Covertype and Buzz datasets [Dheeru and Karra Taniskidou, 2017] with normalized rows for each dataset. ... UCI machine learning repository, 2017. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or testing splits for the datasets used in the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | We choose ํ= 105, ํ= 40, ํ= {5, 10, 15, 20}, ํ= 0.5, ํฟ= 10 4, and ํ= 1. ... We set ํ= 10, ํ= 2000, ํ= 400, ํ = {15, 20, 40, 80} and ํ= 1. |