Localized Differential Location Privacy Protection Scheme in Mobile Environment

被引：1

作者：

Kai, Liu ^{[1
]}

Wang Jingjing ^{[1
]}

Hu Yanjing ^{[1
]}

机构：

[1] Engn Univ PAP, Coll Cryptog Engn, Xian, Peoples R China

来源：

2022 IEEE THE 5TH INTERNATIONAL CONFERENCE ON BIG DATA AND ARTIFICIAL INTELLIGENCE (BDAI 2022) | 2022年

基金：

中国国家自然科学基金;

关键词：

location privacy protection; K-anonymity; differential privacy; Markov model;

D O I：

10.1109/BDAI56143.2022.9862753

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

When users request location services, they are easy to expose their privacy information, and the scheme of using a third-party server for location privacy protection has high requirements for the credibility of the server. To solve these problems, a localized differential privacy protection scheme in mobile environment is proposed, which uses Markov chain model to generate probability transition matrix, and adds Laplace noise to construct a location confusion function that meets differential privacy, Conduct location confusion on the client, construct and upload anonymous areas. Through the analysis of simulation experiments, the scheme can solve the problem of untrusted thirdparty server, and has high efficiency while ensuring the high availability of the generated anonymous area.

引用

页码：148 / 152

页数：5

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