License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2023.39
URN: urn:nbn:de:0030-drops-175426
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17542/
Cohen, Edith ;
Lyu, Xin ;
Nelson, Jelani ;
Sarlós, Tamás ;
Stemmer, Uri
Generalized Private Selection and Testing with High Confidence
Abstract
Composition theorems are general and powerful tools that facilitate privacy accounting across multiple data accesses from per-access privacy bounds. However they often result in weaker bounds compared with end-to-end analysis. Two popular tools that mitigate that are the exponential mechanism (or report noisy max) and the sparse vector technique, generalized in a recent private selection framework by Liu and Talwar (STOC 2019). In this work, we propose a flexible framework of private selection and testing that generalizes the one proposed by Liu and Talwar, supporting a wide range of applications. We apply our framework to solve several fundamental tasks, including query releasing, top-k selection, and stable selection, with improved confidence-accuracy tradeoffs. Additionally, for online settings, we apply our private testing to design a mechanism for adaptive query releasing, which improves the sample complexity dependence on the confidence parameter for the celebrated private multiplicative weights algorithm of Hardt and Rothblum (FOCS 2010).
BibTeX - Entry
@InProceedings{cohen_et_al:LIPIcs.ITCS.2023.39,
author = {Cohen, Edith and Lyu, Xin and Nelson, Jelani and Sarl\'{o}s, Tam\'{a}s and Stemmer, Uri},
title = {{Generalized Private Selection and Testing with High Confidence}},
booktitle = {14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
pages = {39:1--39:23},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-263-1},
ISSN = {1868-8969},
year = {2023},
volume = {251},
editor = {Tauman Kalai, Yael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/17542},
URN = {urn:nbn:de:0030-drops-175426},
doi = {10.4230/LIPIcs.ITCS.2023.39},
annote = {Keywords: differential privacy, sparse vector technique, adaptive data analysis}
}
Keywords: |
|
differential privacy, sparse vector technique, adaptive data analysis |
Collection: |
|
14th Innovations in Theoretical Computer Science Conference (ITCS 2023) |
Issue Date: |
|
2023 |
Date of publication: |
|
01.02.2023 |