多级本地化差分隐私算法推荐框架  

Multi-level local differential privacy algorithm recommendation framework

作　　者：王瀚仪[1,2] 李效光 毕文卿 陈亚虹 李凤华 牛犇[1] WANG Hanyi;LI Xiaoguang;BI Wenqing;CHEN Yahong;LI Fenghua;NIU Ben(Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China;School of Cyber Security,University of Chinese Academy of Sciences,Beijing 100049,China;School of Cyber Engineering,Xidian University,Xi’an 710071,China)

机构地区：[1]中国科学院信息工程研究所,北京100093 [2]中国科学院大学网络空间安全学院,北京100049 [3]西安电子科技大学网络与信息安全学院,陕西西安710071

出　　处：《通信学报》2022年第8期52-64,共13页Journal on Communications

基　　金：国家重点研发计划基金资助项目(No.2021YFB3100300);国家自然科学基金资助项目(No.61872441,No.61932015)。

摘　　要：本地化差分隐私(LDP)算法通常为不同用户分配相同的保护机制及参数,却忽视了不同用户终端设备资源与隐私需求的差异。为此,提出一种多级LDP算法推荐框架。该框架考虑服务商以及用户的需求,通过服务商和用户的多级管理实现多用户差异化隐私保护。将框架应用至频数统计场景形成LDP算法推荐方案,改进LDP算法以保证统计结果的可用性,设计协同机制保护用户的隐私偏好。实验结果证明了所提方案的可用性。Local differential privacy(LDP)algorithm usually assigned the same protection mechanism and parameters to different users.However,it ignored the differences among the device resources and the privacy requirements of different users.For this reason,a multi-level LDP algorithm recommendation framework was proposed.The server and the users’requirements were considered in the framework,and the multi-users’differential privacy protections were realized by the server and the users’multi-level management.The framework was applied to the frequency statistics scenario to form an LDP algorithm recommendation scheme.LDP algorithm was improved to ensure the availability of statistical results,and a collaborative mechanism was designed to protect users’privacy preferences.The experimental results demonstrate the availability of the proposed scheme.

关 键 词：本地化差分隐私 资源自适应 个性化隐私预算 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]