Differentially Private Linear Regression with Linked Data. (arXiv:2308.00836v1 [stat.ME])

There has been increasing demand for establishing privacy-preserving
methodologies for modern statistics and machine learning. Differential privacy,
a mathematical notion from computer science, is a rising tool offering robust
privacy guarantees. Recent work focuses primarily on developing differentially
private versions of individual statistical and machine learning tasks, with
nontrivial upstream pre-processing typically not incorporated. An important
example is when record linkage is done prior to downstream modeling. Record
linkage refers to the statistical task of linking two or more data …

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