Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems

2018 ◽  
Vol 135 ◽  
pp. 32-43 ◽  
Author(s):  
Yingjie Wang ◽  
Zhipeng Cai ◽  
Xiangrong Tong ◽  
Yang Gao ◽  
Guisheng Yin
Keyword(s):  
Location Privacy ◽  
Incentive Mechanism ◽  
Privacy Preserving ◽  
Mobile Crowdsourcing

scholarly journals The Novel Location Privacy-Preserving CKD for Mobile Crowdsourcing Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 5678-5687 ◽  
Author(s):  
Zhongyang Chi ◽  
Yingjie Wang ◽  
Yan Huang ◽  
Xiangrong Tong
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
The Novel ◽  
Mobile Crowdsourcing

scholarly journals A Survey on Location Privacy-Preserving Mechanisms in Mobile Crowdsourcing

2019 ◽  
Vol 10 (7) ◽  
Author(s):  
Arwa Bashanfar ◽  
Eman Al-Zahrani ◽  
Maram Alutebei ◽  
Wejdan Aljagthami ◽  
Suhari Alshehri
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsourcing

scholarly journals Mobility-Aware Privacy-Preserving Mobile Crowdsourcing

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2474
Author(s):  
Guoying Qiu ◽  
Yulong Shen ◽  
Ke Cheng ◽  
Lingtong Liu ◽  
Shuiguang Zeng
Keyword(s):  
Private Information ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile User ◽  
Unbiased Estimation ◽  
Transfer Model ◽  
Trajectory Prediction ◽  
Mobile Crowdsourcing ◽  
Time Partitioning

The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant’s location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant’s location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user’s time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant’s dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability.

scholarly journals A differentially k-anonymity-based location privacy-preserving for mobile crowdsourcing systems

2018 ◽  
Vol 129 ◽  
pp. 28-34 ◽  
Author(s):  
Yingjie Wang ◽  
Zhipeng Cai ◽  
Zhongyang Chi ◽  
Xiangrong Tong ◽  
Lijie Li
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsourcing

When Crowdsourcing Meets Social IoT: An Efficient Privacy-Preserving Incentive Mechanism

2019 ◽  
Vol 6 (6) ◽  
pp. 9707-9721 ◽  
Author(s):  
Xiaoying Gan ◽  
Yuqing Li ◽  
Yixuan Huang ◽  
Luoyi Fu ◽  
Xinbing Wang
Keyword(s):  
Incentive Mechanism ◽  
Privacy Preserving

Location Privacy-Preserving Task Recommendation with Geometric Range Query in Mobile Crowdsensing

2021 ◽  
pp. 1-1
Author(s):  
Chuan Zhang ◽  
Liehuang Zhu ◽  
Chang Xu ◽  
Jianbing Ni ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

Efficient location privacy-preserving range query scheme for vehicle sensing systems

2020 ◽  
Vol 106 ◽  
pp. 101714 ◽  
Author(s):  
Peng Hu ◽  
Yongli Wang ◽  
Quanbing Li ◽  
Yongjian Wang ◽  
Yanchao Li ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Sensing Systems

scholarly journals A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoguang Niu ◽  
Jiawei Wang ◽  
Qiongzan Ye ◽  
Yihao Zhang
Keyword(s):  
Dynamic Balance ◽  
Incentive Mechanism ◽  
Privacy Preserving ◽  
Circulation System ◽  
Participatory Sensing ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Ideal Situation ◽  
Conflicting Objectives ◽  
Share Information

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

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