Title
Incentivizing Verifiable Privacy-Protection Mechanisms for Offline Crowdsensing Applications.
Abstract
Incentive mechanisms of crowdsensing have recently been intensively explored. Most of these mechanisms mainly focus on the standard economical goals like truthfulness and utility maximization. However, enormous privacy and security challenges need to be faced directly in real-life environments, such as cost privacies. In this paper, we investigate offline verifiable privacy-protection crowdsensing issues. We firstly present a general verifiable privacy-protection incentive mechanism for the offline homogeneous and heterogeneous sensing job model. In addition, we also propose a more complex verifiable privacy-protection incentive mechanism for the offline submodular sensing job model. The two mechanisms not only explore the private protection issues of users and platform, but also ensure the verifiable correctness of payments between platform and users. Finally, we demonstrate that the two mechanisms satisfy privacy-protection, verifiable correctness of payments and the same revenue as the generic one without privacy protection. Our experiments also validate that the two mechanisms are both scalable and efficient, and applicable for mobile devices in crowdsensing applications based on auctions, where the main incentive for the user is the remuneration.
Year
DOI
Venue
2017
10.3390/s17092024
SENSORS
Keywords
Field
DocType
mobile crowdsensing,privacy protection,verifiable correctness,incentive mechanisms
Remuneration,Incentive,Computer security,Correctness,Submodular set function,Verifiable secret sharing,Common value auction,Mobile device,Engineering,Scalability
Journal
Volume
Issue
ISSN
17
9.0
1424-8220
Citations 
PageRank 
References 
0
0.34
12
Authors
2
Name
Order
Citations
PageRank
Jiajun Sun112.06
Ningzhong Liu2178.34