Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Principal Component Analysis in the Local Differential Privacy Model
Authors: Di Wang, Jinhui Xu
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,416 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,219 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |