A Privacy-Preserving Pedestrian Dead Reckoning Framework Based on Differential Privacy

Tianyi Feng, Zhixiang Zhang, L. W. Wong, Sumei Sun, B. Sikdar
{"title":"A Privacy-Preserving Pedestrian Dead Reckoning Framework Based on Differential Privacy","authors":"Tianyi Feng, Zhixiang Zhang, L. W. Wong, Sumei Sun, B. Sikdar","doi":"10.1109/pimrc50174.2021.9569650","DOIUrl":null,"url":null,"abstract":"Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/pimrc50174.2021.9569650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.
基于差分隐私保护的行人航位推算框架
行人航位推算(PDR)是一种广泛使用的定位和轨迹估计方法。利用轨迹数据访问基于位置的服务可以为人们带来便利,但也可能引发需要解决的隐私问题。为了保护用户的轨迹隐私,提出了一种基于差分隐私的行人航位推算框架。我们引入了两个指标来量化轨迹隐私和数据效用。我们提出的隐私保护轨迹提取算法包括初始位置、步幅和方向三种机制。此外,我们设计了一个基于粒子滤波的对手模型来评估性能,并利用我们收集的传感器读取数据集证明我们提出的框架的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信