SoK: A Review of Differentially Private Linear Models For High-Dimensional Data

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2404.01141v1.pdf (785.58 KB)

Links to Files

https://arxiv.org/abs/2404.01141v1

Permanent Link

https://doi.org/10.48550/arXiv.2404.01141
 http://hdl.handle.net/11603/33597

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UMBC Faculty Collection
UMBC Data Science

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Author/Creator ORCID

https://orcid.org/0000-0002-9900-1972

Date

2024-04-01

Type of Work

journal articles
preprints
Text

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Citation of Original Publication

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differential privacy can be used. Many papers have proposed optimization techniques for high-dimensional differentially private linear models, but a systematic comparison between these methods does not exist. We close this gap by providing a comprehensive review of optimization methods for private high-dimensional linear models. Empirical tests on all methods demonstrate robust and coordinate-optimized algorithms perform best, which can inform future research. Code for implementing all methods is released online.