On Computing Pairwise Statistics with Local Differential Privacy
Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give several novel and generic algorithms for the problem, leveraging techniques from DP algorithms for linear queries. ... In this work, we answer this question by establishing connections between the problems of computing quadratic forms and linear queries. Leveraging the wealth of knowledge on the latter, we obtain several new (and general) upper and lower bounds for the former. |
| Researcher Affiliation | Industry | Badih Ghazi Google Research Mountain View, CA, US badihghazi@gmail.com Pritish Kamath Google Research Mountain View, CA, US pritish@alum.mit.edu Ravi Kumar Google Research Mountain View, CA, US ravi.k53@gmail.com Pasin Manurangsi Google Research Bangkok, Thailand pasin@google.com Adam Sealfon Google Research New York, NY, US adamsealfon@google.com |
| Pseudocode | No | The paper describes algorithms in prose within sections like "Algorithm Description" but does not present them in formal pseudocode blocks or clearly labeled algorithm environments. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements about code availability. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets; therefore, no information about publicly available training data is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving datasets; therefore, no information about training/validation/test splits is provided. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on any empirical experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not report on any empirical experiments that would require specific software dependencies, thus no such information is provided. |
| Experiment Setup | No | The paper focuses on theoretical contributions and does not describe an experimental setup with hyperparameters or system-level training settings. |