Differentially Private Clustering via Maximum Coverage
Authors: Matthew Jones, Huy L. Nguyen, Thy D Nguyen11555-11563
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. Additionally, our algorithms use a clustering algorithm without differential privacy as a black-box. |
| Researcher Affiliation | Academia | Matthew Jones, Huy L. Nguyen, Thy D Nguyen Northeastern University {jones.m, hu.nguyen, nguyen.thy2}@northeastern.edu |
| Pseudocode | Yes | Algorithm 1: Maximum Coverage |
| 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 describes theoretical algorithms and does not mention using any specific publicly available datasets or provide access information for data. |
| Dataset Splits | No | This paper is theoretical and does not describe experiments with data, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental execution, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental execution, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithms, not empirical experiments. Therefore, it does not provide specific experimental setup details like hyperparameters or training configurations. |