Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
Authors: Yi Liu, Ke Sun, Bei Jiang, Linglong Kong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | There are no experimental results in this work. Figures are only for demonstrative reasons as main results are supported by proofs. |
| Researcher Affiliation | Academia | Yi Liu Department of Mathematical and Statistical Sciences University of Alberta yliu16@ualberta.ca Ke Sun Department of Mathematical and Statistical Sciences University of Alberta ksun6@ualberta.ca Bei Jiang Department of Mathematical and Statistical Sciences University of Alberta bei1@ualberta.ca Linglong Kong Department of Mathematical and Statistical Sciences University of Alberta lkong@ualberta.ca |
| Pseudocode | Yes | Algorithm 1: Finding ยต with privacy profiles (optimized). |
| Open Source Code | No | Under the 'If you ran experiments...' section, the authors state 'There are no experimental results in this work. Figures are only for demonstrative reasons as main results are supported by proofs.', which implies no code is provided for experimental reproduction. There is no explicit statement or link indicating the release of source code for the theoretical methodology described in the paper. |
| Open Datasets | No | The paper states, 'There are no experimental results in this work. Figures are only for demonstrative reasons as main results are supported by proofs.', which means no datasets were used for training or evaluation in an experimental setting, and therefore no information about their public availability is provided. |
| Dataset Splits | No | The paper explicitly states 'There are no experimental results in this work.', which implies no dataset splits for training, validation, or testing were used or reported. |
| Hardware Specification | No | The paper states 'The amount of computing resource needed is negligible.' and does not provide any specific hardware details as no experiments were conducted. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers required to replicate any experiments. |
| Experiment Setup | No | The paper states 'There are no experimental results in this work. Figures are only for demonstrative reasons as main results are supported by proofs.', meaning no experimental setup details or hyperparameters are provided. |