Location Privacy-Preserving Data Recovery for Mobile Crowdsensing

2018 ◽  
Vol 2 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Tongqing Zhou ◽  
Zhiping Cai ◽  
Bin Xiao ◽  
Leye Wang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Data Recovery ◽  
Mobile Crowdsensing

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

scholarly journals Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation

2017 ◽  
Author(s):  
Leye Wang ◽  
Dingqi Yang ◽  
Xiao Han ◽  
Tianben Wang ◽  
Daqing Zhang ◽  
...  
Keyword(s):  
Task Allocation ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

scholarly journals From Centralized Protection to Distributed Edge Collaboration: A Location Difference-Based Privacy-Preserving Framework for Mobile Crowdsensing

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zihao Shao ◽  
Huiqiang Wang ◽  
Yifan Zou ◽  
Zihan Gao ◽  
Hongwu Lv
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Secure Computation ◽  
User Characteristics ◽  
User Privacy ◽  
Mobile Crowdsensing ◽  
Data Coding ◽  
Sensing Platform ◽  
Single Region ◽  
The Impact

Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.

Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey

2022 ◽  
pp. 103315
Author(s):  
Jong Wook Kim ◽  
Kennedy Edemacu ◽  
Beakcheol Jang
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

scholarly journals CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System

2020 ◽  
Vol 16 (6) ◽  
pp. 4206-4218 ◽  
Author(s):  
Shihong Zou ◽  
Jinwen Xi ◽  
Honggang Wang ◽  
Guoai Xu
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

scholarly journals COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing

2020 ◽  
Author(s):  
Rui Chen ◽  
Liang Li ◽  
Jeffrey Jiarui Chen ◽  
Ronghui Hou ◽  
Yanmin Gong ◽  
...  
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsourcing ◽  
Map Construction ◽  
Vulnerability Map

Accurate and Privacy-Preserving Task Allocation for Edge Computing Assisted Mobile Crowdsensing

2021 ◽  
pp. 1-14
Author(s):  
Zhihua Wang ◽  
Chaoqi Guo ◽  
Jiahao Liu ◽  
Jiamin Zhang ◽  
Yongjian Wang ◽  
...  
Keyword(s):  
Task Allocation ◽  
Privacy Preserving ◽  
Edge Computing ◽  
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

Optimizing Task Location Privacy in Mobile Crowdsensing Systems

2021 ◽  
pp. 1-1
Author(s):  
Xuewen Dong ◽  
Wen Zhang ◽  
Yushu Zhang ◽  
Zhichao You ◽  
Sheng Gao ◽  
...  
Keyword(s):  
Location Privacy ◽  
Mobile Crowdsensing
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