When Mobile Crowdsensing Meets Privacy

被引：48

作者：

Wang, Zhibo ^{[1
]}

Pang, Xiaoyi ^{[1
]}

Hu, Jiahui ^{[1
]}

Liu, Wenxin ^{[1
]}

Wang, Qian ^{[1
]}

Li, Yanjun ^{[2
]}

Chen, Honglong ^{[3
]}

机构：

[1] Wuhan Univ, Wuhan, Hubei, Peoples R China

[2] Zhejiang Univ Technol, Hangzhou, Zhejiang, Peoples R China

[3] China Univ Petr, Beijing, Peoples R China

来源：

IEEE COMMUNICATIONS MAGAZINE | 2019年 / 57卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Task analysis; Wireless sensor networks; Privacy; Resource management; Data privacy; Internet of Things; Data collection;

D O I：

10.1109/MCOM.001.1800674

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations. Although several works on MCS have explored the privacy issues in task allocation, incentive, and data reporting, there is still a lack of a comprehensive privacy preserving framework for MCS to protect the privacy of users throughout users' involvement in crowdsensing tasks. In this article, we divide the life cycle of each crowdsensing task in MCS into four phases: task allocation, incentive, data collection, and data publishing, and design a privacy-preserving framework for MCS to protect users' privacy in the whole life cycle of MCS.

引用

页码：72 / 78

页数：7

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