On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

Autor: Julie A. McCann, Shusen Yang, Cong Zhao
Přispěvatelé: The Alan Turing Institute
Rok vydání: 2021
Předmět:
Technology
Information privacy
Computer Networks and Communications
Computer science
media_common.quotation_subject
0805 Distributed Computing
02 engineering and technology
Computer security
computer.software_genre
Crowdsensing
Data integrity
1005 Communications Technologies
0202 electrical engineering
electronic engineering
information engineering
data quality
Quality (business)
untruthful reporting
Electrical and Electronic Engineering
Private information retrieval
media_common
Science & Technology
Computer Science
Information Systems
020206 networking & telecommunications
Mobile crowdsensing systems
privacy preservation
0906 Electrical and Electronic Engineering
Data quality
Computer Science
Telecommunications
Networking & Telecommunications
Mobile device
computer
Game theory
Software
Algorithmic mechanism design
Zdroj: IEEE Transactions on Mobile Computing. 20:647-661
ISSN: 2161-9875
1536-1233
Popis: 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.
Databáze: OpenAIRE
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