Differentially Private Sampling from Distributions
Authors: Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide tight upper and lower bounds for the dataset size needed for this task for three natural families of distributions: arbitrary distributions on {1, . . . , k}, arbitrary product distributions on {0, 1}d, and product distributions on on {0, 1}d with bias in each coordinate bounded away from 0 and 1. We demonstrate that, in some parameter regimes, private sampling requires asymptotically fewer observations than learning a description of P nonprivately; in other regimes, however, private sampling proves to be as difficult as private learning. |
| Researcher Affiliation | Academia | Sofya Raskhodnikova Department of Computer Science Boston University EMAIL Satchit Sivakumar Department of Computer Science Boston University EMAIL Adam Smith Department of Computer Science Boston University EMAIL Marika Swanberg Department of Computer Science Boston University EMAIL |
| Pseudocode | No | The paper describes algorithms and proof techniques in prose but does not provide structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and bounds for distribution classes (e.g., 'distributions on {1, . . . , k}'), rather than conducting experiments on specific, named datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments that would involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with hyperparameters or system-level training settings. |