.png){kind=icon}
返回
Efficient Sparse Least Absolute Deviation Regression With Differential Privacy
.png){kind=icon}
OA
AI
DOI：10.1109/TIFS.2023.3349054.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, privacy-preserving machine learning algorithms have attracted increasing attention because of their important applications in many scientific fields. However, in the literature, most privacy-preserving algorithms demand learning objectives to be strongly convex and Lipschitz smooth, which thus cannot cover a wide class of robust loss functions (e.g., quantile/least absolute loss). In this work, we aim to develop a fast privacy-preserving learning solution for a sparse robust regression problem. Our learning loss consists of a robust least absolute loss and an & ell;(1) sparse penalty term. To fast solve the non-smooth loss under a given privacy budget, we develop a Fast Robust And Privacy-Preserving Estimation (FRAPPE) algorithm for least absolute deviation regression. Our algorithm achieves a fast estimation by reformulating the sparse LAD problem as a penalized least square estimation problem and adopts a three-stage noise injection to guarantee the (& varepsilon;,delta) -differential privacy. We show that our algorithm can achieve better privacy and statistical accuracy trade-off compared with the state-of-the-art privacy-preserving regression algorithms. In the end, we conduct experiments to verify the efficiency of our proposed FRAPPE algorithm.
Keyword:
Robust regression
sparse learning
differential privacy
least absolute deviation
期刊
IF:
8
论文数:
5.3K
被引数:
2.3W
机构
引用论文
Comparison of three methods for estimating the numbers of common rats (Rattus norvegicus).png)
Mammalia
MammaliaIF0
PREAI
分享收藏
查看更多内容