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2024 | OriginalPaper | Buchkapitel

Differentially Private Quantile Regression

verfasst von : Tran Tran, Matthew Reimherr, Aleksandra Slavkovic

Erschienen in: Privacy in Statistical Databases

Verlag: Springer Nature Switzerland

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Abstract

Quantile regression (QR) is a powerful and robust statistical modeling method broadly used in many fields such as economics, ecology, and healthcare. However, it has not been well-explored in differential privacy (DP) since its loss function lacks strong convexity and twice differentiability, often required by many DP mechanisms. We implement the smoothed QR loss via convolution within the K-Norm Gradient mechanism (KNG) and prove the resulting estimate converges to the non-private one asymptotically. Additionally, our work is the first to extensively investigate the empirical performance of DP smoothing QR under pure-, approximate- and concentrated-DP and four mechanisms, and cases commonly encountered in practice such as heavy-tailed and heteroscedastic data. We find that the Objective Perturbation Mechanism and KNG are the top performers across the simulated settings.

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Vorheriges Kapitel From Isolation to Identification
Nächstes Kapitel Utility Analysis of Differentially Private Anonymized Data Based on Random Sampling
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Metadaten
Titel
Differentially Private Quantile Regression
verfasst von
Tran Tran
Matthew Reimherr
Aleksandra Slavkovic
Copyright-Jahr
2024
Buch
Privacy in Statistical Databases

Print ISBN: 978-3-031-69650-3

Electronic ISBN: 978-3-031-69651-0

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-69651-0_2

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