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Differentially Private Linear Regression with Linked Data
May 9, 2024, 4:12 a.m. | Shurong Lin, Elliot Paquette, Eric D. Kolaczyk
cs.CR updates on arXiv.org arxiv.org
Abstract: 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 …
arxiv computer computer science cs.cr data demand differential privacy linear machine machine learning methodologies notion privacy private rising science statistics stat.me tool work
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A Privacy Preserving System for Movie Recommendations Using Federated Learning
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