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.

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 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

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 blockchain-based location privacy-preserving crowdsensing system

2019 ◽  
Vol 94 ◽  
pp. 408-418 ◽  
Author(s):  
Mengmeng Yang ◽  
Tianqing Zhu ◽  
Kaitai Liang ◽  
Wanlei Zhou ◽  
Robert H. Deng
Keyword(s):  
Location Privacy ◽  
Privacy Preserving

scholarly journals A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

2018 ◽  
Vol 129 ◽  
pp. 368-371 ◽  
Author(s):  
Lina Ni ◽  
Yanfeng Yuan ◽  
Xiao Wang ◽  
Mengmeng Zhang ◽  
Jinquan Zhang
Keyword(s):  
Social Network ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Social Network
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