Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy
In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocat...
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doaj-65b5cecb615e4b71885b6a0f73424c0d2021-03-29T20:39:02ZengIEEEIEEE Access2169-35362018-01-016147791478910.1109/ACCESS.2018.28169188319407Density-Based Location Preservation for Mobile Crowdsensing With Differential PrivacyMengmeng Yang0Tianqing Zhu1https://orcid.org/0000-0003-3411-7947Yang Xiang2Wanlei Zhou3School of Information Technology, Deakin University, Melbourne, VIC, AustraliaSchool of Information Technology, Deakin University, Melbourne, VIC, AustraliaDigital Research and Innovation Capability Platform, Swinburne University, Melbourne, VIC, AustraliaSchool of Information Technology, Deakin University, Melbourne, VIC, AustraliaIn recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocation of tasks, a centralized server needs to know the precise location of each user, but exposing the workers' exact locations raises privacy concerns. In this paper, we propose a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations. The partitioning method is based on worker density and considers non-uniform worker distribution. In addition, we propose a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads. Extensive experiments prove that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.https://ieeexplore.ieee.org/document/8319407/Crowdsensingdifferential privacylocation privacy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mengmeng Yang Tianqing Zhu Yang Xiang Wanlei Zhou |
spellingShingle |
Mengmeng Yang Tianqing Zhu Yang Xiang Wanlei Zhou Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy IEEE Access Crowdsensing differential privacy location privacy |
author_facet |
Mengmeng Yang Tianqing Zhu Yang Xiang Wanlei Zhou |
author_sort |
Mengmeng Yang |
title |
Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy |
title_short |
Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy |
title_full |
Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy |
title_fullStr |
Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy |
title_full_unstemmed |
Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy |
title_sort |
density-based location preservation for mobile crowdsensing with differential privacy |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocation of tasks, a centralized server needs to know the precise location of each user, but exposing the workers' exact locations raises privacy concerns. In this paper, we propose a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations. The partitioning method is based on worker density and considers non-uniform worker distribution. In addition, we propose a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads. Extensive experiments prove that the proposed method not only provides a strict privacy guarantee but also significantly improves performance. |
topic |
Crowdsensing differential privacy location privacy |
url |
https://ieeexplore.ieee.org/document/8319407/ |
work_keys_str_mv |
AT mengmengyang densitybasedlocationpreservationformobilecrowdsensingwithdifferentialprivacy AT tianqingzhu densitybasedlocationpreservationformobilecrowdsensingwithdifferentialprivacy AT yangxiang densitybasedlocationpreservationformobilecrowdsensingwithdifferentialprivacy AT wanleizhou densitybasedlocationpreservationformobilecrowdsensingwithdifferentialprivacy |
_version_ |
1724194361199034368 |