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Differentially Private Significance Tests for Regression Coefficients

Content Provider	Scilit
Author	Barrientos, Andrés F. Reiter, Jerome P. Machanavajjhala, Ashwin Chen, Yan
Copyright Year	2019
Description	Journal: Journal of Computational and Graphical Statistics Many data producers seek to provide users access to confidential data without unduly compromising data subjects' privacy and confidentiality. One general strategy is to require users to do analyses without seeing the confidential data; for example, analysts only get access to synthetic data or query systems that provide disclosure-protected outputs of statistical models. With synthetic data or redacted outputs, the analyst never really knows how much to trust the resulting findings. In particular, if the user did the same analysis on the confidential data, would regression coefficients of interest be statistically significant or not? We present algorithms for assessing this question that satisfy differential privacy. We describe conditions under which the algorithms should give accurate answers about statistical significance. We illustrate the properties of the proposed methods using artificial and genuine data. Supplementary materials for this article are available online.
Related Links	http://arxiv.org/pdf/1705.09561
Ending Page	453
Page Count	14
Starting Page	440
ISSN	10618600
e-ISSN	15372715
DOI	10.1080/10618600.2018.1538881
Journal	Journal of Computational and Graphical Statistics
Issue Number	2
Volume Number	28
Language	English
Publisher	Informa UK Limited
Publisher Date	2019-02-27
Access Restriction	Open
Subject Keyword	Journal: Journal of Computational and Graphical Statistics Statistics and Probability Confidentiality Verification
Content Type	Text
Subject	Statistics and Probability Discrete Mathematics and Combinatorics Statistics, Probability and Uncertainty

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