On Convex Optimization with Semi-Sensitive Features
<|reference_start|>On Convex Optimization with Semi-Sensitive Features: 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).<|reference_end|>
@article{ghazi2024on,
    title={On Convex Optimization with Semi-Sensitive Features},
    author={Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka,
  Chiyuan Zhang},
    journal={arXiv preprint arXiv:2406.19040},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19040},
    primaryClass={cs.LG cs.CR cs.DS}
}