基于差分隐私的分布式组变量选择
Distributed group variable selection based on differential privacy
李子涵 1张海1
作者信息
- 1. 西北大学数学学院,陕西西安 710127
- 折叠
摘要
针对组结构数据的隐私保护问题,通过随机响应机制,对原始数据进行扰动,开展满足差分隐私的分布式组变量选择研究.首先基于交替方向乘子法,提出了分布式Logistic组变量选择算法.进一步为了防止计算机信息交互过程中可能产生的隐私泄露,提出了分布式Logistic随机响应组变量选择算法,并证明算法满足差分隐私.实验表明,所提算法可有效处理组结构分类数据并保护其隐私.
Abstract
The privacy protection of data with group structure is focused,disturbs the original data through randomized response mechanism,and carries out the research on the distributed group variable selection that satisfies differential privacy.First,based on the alternating direction method of multipliers,a distributed Logistic group variable selection algorithm is proposed.Furthermore,in order to prevent possible privacy leakage in the process of computer information interaction,a distributed Logistic randomized response group variable selection algorithm is proposed,and it is proved that the algorithm satisfies differential privacy.Experimental results show that the algorithm can effectively deal with the group structure classification data and protect its privacy.
关键词
组变量选择/差分隐私/随机响应机制/Logistic回归/分布式算法Key words
variable selection/differential privacy/randomized response/Logistic regression/distributed algorithm引用本文复制引用
基金项目
NSFC-广东省大数据重大项目(U1811461)
出版年
2024
国家科技期刊平台
NSTL
万方数据