Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/510544
Title: The Accuracy-Privacy Trade-off of Mobile Crowdsensing
Authors: Mohammad Abu Alsheikh;Yutao Jiao;Dusit Niyato;Ping Wang;Derek Leong;Zhu Han
Year: 2017
Publisher: IEEE
Abstract: Mobile crowdsensing has emerged as an efficient sensing paradigm that combines the crowd intelligence and the sensing power of mobile devices, such as mobile phones and Internet of Things gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users, and accuracy maximization and collection of true data by service providers. We first define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the service provider from buying their data. We then propose a truthful mechanism for achieving high service accuracy while protecting privacy based on user preferences. The users are incentivized to provide true data by being paid based on their individual contribution to the overall service accuracy. Moreover, we propose a coalition strategy that allows users to cooperate in providing their data under one identity, increasing their anonymity privacy protection, and sharing the resulting payoff. Finally, we outline important open research directions in mobile and people- centric crowdsensing.
URI: http://dl.kums.ac.ir/handle/Hannan/510544
volume: 55
issue: 6
More Information: 132,
139
Appears in Collections:2017

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Title: The Accuracy-Privacy Trade-off of Mobile Crowdsensing
Authors: Mohammad Abu Alsheikh;Yutao Jiao;Dusit Niyato;Ping Wang;Derek Leong;Zhu Han
Year: 2017
Publisher: IEEE
Abstract: Mobile crowdsensing has emerged as an efficient sensing paradigm that combines the crowd intelligence and the sensing power of mobile devices, such as mobile phones and Internet of Things gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users, and accuracy maximization and collection of true data by service providers. We first define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the service provider from buying their data. We then propose a truthful mechanism for achieving high service accuracy while protecting privacy based on user preferences. The users are incentivized to provide true data by being paid based on their individual contribution to the overall service accuracy. Moreover, we propose a coalition strategy that allows users to cooperate in providing their data under one identity, increasing their anonymity privacy protection, and sharing the resulting payoff. Finally, we outline important open research directions in mobile and people- centric crowdsensing.
URI: http://dl.kums.ac.ir/handle/Hannan/510544
volume: 55
issue: 6
More Information: 132,
139
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7946934.pdf758.01 kBAdobe PDFThumbnail
Preview File
Title: The Accuracy-Privacy Trade-off of Mobile Crowdsensing
Authors: Mohammad Abu Alsheikh;Yutao Jiao;Dusit Niyato;Ping Wang;Derek Leong;Zhu Han
Year: 2017
Publisher: IEEE
Abstract: Mobile crowdsensing has emerged as an efficient sensing paradigm that combines the crowd intelligence and the sensing power of mobile devices, such as mobile phones and Internet of Things gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users, and accuracy maximization and collection of true data by service providers. We first define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the service provider from buying their data. We then propose a truthful mechanism for achieving high service accuracy while protecting privacy based on user preferences. The users are incentivized to provide true data by being paid based on their individual contribution to the overall service accuracy. Moreover, we propose a coalition strategy that allows users to cooperate in providing their data under one identity, increasing their anonymity privacy protection, and sharing the resulting payoff. Finally, we outline important open research directions in mobile and people- centric crowdsensing.
URI: http://dl.kums.ac.ir/handle/Hannan/510544
volume: 55
issue: 6
More Information: 132,
139
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7946934.pdf758.01 kBAdobe PDFThumbnail
Preview File