Imperial College London
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ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Vice-Dean (Research) for the Faculty of Engineering
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

260ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhao:2021:10.1109/tmc.2019.2943468,
author = {Zhao, C and Yang, S and McCann, JA},
doi = {10.1109/tmc.2019.2943468},
journal = {IEEE Transactions on Mobile Computing},
pages = {647--661},
title = {On the data quality in privacy-preserving mobile crowdsensing systems with untruthful reporting},
url = {http://dx.doi.org/10.1109/tmc.2019.2943468},
volume = {20},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The proliferation of mobile smart devices with ever improving sensing capacities means that human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large scale and flexible sensing solution. The use of personal mobile devices is a sensitive issue, therefore it is mandatory for practical MCSs to preserve private information (the user's true identity, precise location, etc.) while collecting the required sensing data. However, well intentioned privacy protection techniques also conceal autonomous, or even malicious, behaviors of device owners (termed as self-interested), where the objectivity and accuracy of crowdsensing data can therefore be severely threatened. The issue of data quality due to untruthful reporting in privacy-preserving MCSs has been yet to produce solutions. Bringing together game theory, algorithmic mechanism design, and truth discovery, we develop a mechanism to guarantee and enhance the quality of crowdsensing data without jeopardizing the privacy of MCS participants. Together with solid theoretical justifications, we evaluate the performance of our proposal with extensive real-world MCS trace-driven simulations. Experimental results demonstrate the effectiveness of our mechanism on both enhancing the quality of the crowdsensing data and eliminating the motivation of MCS participants, even when their privacy is well protected, to report untruthfully.
AU  - Zhao,C
AU  - Yang,S
AU  - McCann,JA
DO  - 10.1109/tmc.2019.2943468
EP  - 661
PY  - 2021///
SN  - 1536-1233
SP  - 647
TI  - On the data quality in privacy-preserving mobile crowdsensing systems with untruthful reporting
T2  - IEEE Transactions on Mobile Computing
UR  - http://dx.doi.org/10.1109/tmc.2019.2943468
UR  - https://ieeexplore.ieee.org/document/8847467
UR  - http://hdl.handle.net/10044/1/73615
VL  - 20
ER  -
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