On Sparse Linear Regression in the Local Differential Privacy Model
Authors: Di Wang, Jinhui Xu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world and synthetic datasets confirm our theoretical analysis. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, USA. Emails: {dwang45,jinhui}@buffalo.edu. |
| Pseudocode | Yes | Algorithm 1 LDP-IHT |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using a 'real world dataset Covertype' but does not provide a formal citation, link, or repository information for accessing it. It also uses synthetic data whose generation process is described, but no public access is provided. |
| Dataset Splits | No | The paper describes data generation but does not provide specific training/validation/test dataset splits, percentages, or sample counts, nor does it mention cross-validation or standard benchmark splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions using 'TFOCS' as a method for choosing the step size but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We assume 퐶= 0.05 in our experiment. We run algorithms Label-LDP-IHT with 휂= 0.2 or 휂= 0.1, 푠= 푠 , 푇= log 푛 푝 , 훿= 10 3 and a random normal Gaussian vector as the initial point to obtain 휃푇. |