Multi-level local differential privacy algorithm recommendation framework

被引：0
|
作者：
Wang H. [1 ,2 ]
Li X. [3 ]
Bi W. [1 ,2 ]
Chen Y. [1 ,2 ]
Li F. [1 ,2 ]
Niu B. [1 ]
机构：
[1] Institute of Information Engineering, Chinese Academy of Sciences, Beijing
[2] School of Cyber Security, University of Chinese Academy of Sciences, Beijing
[3] School of Cyber Engineering, Xidian University, Xi’an
来源：
Tongxin Xuebao/Journal on Communications | 2022年 / 43卷 / 08期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
local differential privacy; personalized privacy budget; resource adaptation;
D O I：
10.11959/j.issn.1000-436x.2022106
中图分类号：
学科分类号：
摘要：
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. © 2022 Editorial Board of Journal on Communications. All rights reserved.
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收藏
页码：52 / 64
页数：12
相关论文
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