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The Ethereal
On Convex Optimization with Semi-Sensitive Features
June 27, 2024 ยท Declared Dead ยท ๐ Annual Conference Computational Learning Theory
"No code URL or promise found in abstract"
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Authors
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
arXiv ID
2406.19040
Category
cs.LG: Machine Learning
Cross-listed
cs.CR,
cs.DS
Citations
0
Venue
Annual Conference Computational Learning Theory
Last Checked
5 months ago
Abstract
We study the differentially private (DP) empirical risk minimization (ERM) problem under the semi-sensitive DP setting where only some features are sensitive. This generalizes the Label DP setting where only the label is sensitive. We give improved upper and lower bounds on the excess risk for DP-ERM. In particular, we show that the error only scales polylogarithmically in terms of the sensitive domain size, improving upon previous results that scale polynomially in the sensitive domain size (Ghazi et al., 2021).
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