Differentially Private Sampling from Distributions
Authors: Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg
NeurIPS 2021 | Conference 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 sofya@bu.edu Satchit Sivakumar Department of Computer Science Boston University satchit@bu.edu Adam Smith Department of Computer Science Boston University ads22@bu.edu Marika Swanberg Department of Computer Science Boston University marikas@bu.edu |
| 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. |