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Differentially Private Nonparametric Hypothesis Testing

Authors:

Adam GroceAuthors Info & Claims

CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

Pages 737 - 751

https://doi.org/10.1145/3319535.3339821

Published: 06 November 2019 Publication History

Abstract

Hypothesis tests are a crucial statistical tool for data mining and are the workhorse of scientific research in many fields. Here we study differentially private tests of independence between a categorical and a continuous variable. We take as our starting point traditional nonparametric tests, which require no distributional assumption (e.g., normality) about the data distribution. We present private analogues of the Kruskal-Wallis, Mann-Whitney, and Wilcoxon signed-rank tests, as well as the parametric one-sample t-test. These tests use novel test statistics developed specifically for the private setting. We compare our tests to prior work, both on parametric and nonparametric tests. We find that in all cases our new nonparametric tests achieve large improvements in statistical power, even when the assumptions of parametric tests are met.

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Cited By

Lee HSohn MChoi M(2025)Optimising efficiency and patient-centredness in general hospitals: insights from data envelopment and matrix analysisJournal of Health Organization and Management10.1108/JHOM-07-2024-0302Online publication date: 21-Jan-2025
https://doi.org/10.1108/JHOM-07-2024-0302
Awan JWang Z(2025)Simulation-Based, Finite-Sample Inference for Privatized DataJournal of the American Statistical Association10.1080/01621459.2024.2427436(1-14)Online publication date: 3-Jan-2025
https://doi.org/10.1080/01621459.2024.2427436
Kulkarni SKulkarni S(2024)Study of the Value of π Probability Sampling by Testing Hypothesis and ExperimentallyJournal of Computers, Mechanical and Management10.57159/gadl.jcmm.3.1.2401013:1(22-29)Online publication date: 29-Feb-2024
https://doi.org/10.57159/gadl.jcmm.3.1.240101
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Index Terms

Differentially Private Nonparametric Hypothesis Testing
1. Mathematics of computing
  1. Probability and statistics
    1. Nonparametric statistics
2. Security and privacy
  1. Database and storage security
    1. Data anonymization and sanitization

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Published In

cover image ACM Conferences

CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

November 2019

2755 pages

ISBN:9781450367479

DOI:10.1145/3319535

General Chairs:
Lorenzo Cavallaro
King's College London, UK
,
Johannes Kinder
Bundeswehr University Munich, Germany
,
Program Chairs:
XiaoFeng Wang
Indiana University, USA
,
Jonathan Katz
George Mason University, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Published: 06 November 2019

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NSF
Richter Funds

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CCS '19

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SIGSAC

CCS '19: 2019 ACM SIGSAC Conference on Computer and Communications Security

November 11 - 15, 2019

London, United Kingdom

Acceptance Rates

CCS '19 Paper Acceptance Rate 149 of 934 submissions, 16%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Lee HSohn MChoi M(2025)Optimising efficiency and patient-centredness in general hospitals: insights from data envelopment and matrix analysisJournal of Health Organization and Management10.1108/JHOM-07-2024-0302Online publication date: 21-Jan-2025
https://doi.org/10.1108/JHOM-07-2024-0302
Awan JWang Z(2025)Simulation-Based, Finite-Sample Inference for Privatized DataJournal of the American Statistical Association10.1080/01621459.2024.2427436(1-14)Online publication date: 3-Jan-2025
https://doi.org/10.1080/01621459.2024.2427436
Kulkarni SKulkarni S(2024)Study of the Value of π Probability Sampling by Testing Hypothesis and ExperimentallyJournal of Computers, Mechanical and Management10.57159/gadl.jcmm.3.1.2401013:1(22-29)Online publication date: 29-Feb-2024
https://doi.org/10.57159/gadl.jcmm.3.1.240101
Tao YSarwate APanta SPlis SCalhoun V(2024)Privacy-Preserving Visualization of Brain Functional Network Connectivity2024 IEEE International Symposium on Biomedical Imaging (ISBI)10.1109/ISBI56570.2024.10635222(1-5)Online publication date: 27-May-2024
https://doi.org/10.1109/ISBI56570.2024.10635222
Waudby-Smith IWu ZRamdas AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Nonparametric extensions of randomized response for private confidence setsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619936(36748-36789)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619936
Kazan ZShi KGroce ABray AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)The test of testsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619068(16131-16151)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619068
Shen ZZhong TSun HQi B(2023)RRN: A differential private approach to preserve privacy in image classificationIET Image Processing10.1049/ipr2.1278417:7(2192-2203)Online publication date: 20-Mar-2023
https://doi.org/10.1049/ipr2.12784
Li CWang NXu G(2022)Inference for Optimal Differential Privacy Procedures for Frequency TablesJournal of Data Science10.6339/22-JDS1044(253-276)Online publication date: 20-Apr-2022
https://doi.org/10.6339/22-JDS1044
Alabi DMcMillan ASarathy JSmith AVadhan S(2022)Differentially Private Simple Linear RegressionProceedings on Privacy Enhancing Technologies10.2478/popets-2022-00412022:2(184-204)Online publication date: 3-Mar-2022
https://doi.org/10.2478/popets-2022-0041
Liu JXu Y(2022)T-Friedman Test: A New Statistical Test for Multiple Comparison with an Adjustable Conservativeness MeasureInternational Journal of Computational Intelligence Systems10.1007/s44196-022-00083-815:1Online publication date: 30-Apr-2022
https://doi.org/10.1007/s44196-022-00083-8
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