TY - GEN
T1 - Generalized Private Selection and Testing with High Confidence
AU - Cohen, Edith
AU - Lyu, Xin
AU - Nelson, Jelani
AU - Sarlós, Tamás
AU - Stemmer, Uri
N1 - Publisher Copyright:
© Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, and Uri Stemmer; licensed under Creative Commons License CC-BY 4.0.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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).
AB - 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).
KW - adaptive data analysis
KW - differential privacy
KW - sparse vector technique
UR - http://www.scopus.com/inward/record.url?scp=85147538179&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.ITCS.2023.39
DO - 10.4230/LIPIcs.ITCS.2023.39
M3 - Conference contribution
AN - SCOPUS:85147538179
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 14th Innovations in Theoretical Computer Science Conference, ITCS 2023
A2 - Kalai, Yael Tauman
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 14th Innovations in Theoretical Computer Science Conference, ITCS 2023
Y2 - 10 January 2023 through 13 January 2023
ER -