Incentive Mechanism for Privacy-Aware Data Aggregation in Mobile Crowd Sensing Systems
The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation...
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| Published in | IEEE/ACM transactions on networking Vol. 26; no. 5; pp. 2019 - 2032 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from the past literature, we capture such interactive effect and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis as well as extensive simulations. |
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| AbstractList | The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from the past literature, we capture such interactive effect and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis as well as extensive simulations. |
| Author | Su, Lu Xiao, Houping Jin, Haiming Nahrstedt, Klara |
| Author_xml | – sequence: 1 givenname: Haiming orcidid: 0000-0002-3790-3743 surname: Jin fullname: Jin, Haiming email: jinhaiming@sjtu.edu.cn organization: Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA – sequence: 2 givenname: Lu orcidid: 0000-0001-7223-543X surname: Su fullname: Su, Lu email: lusu@buffalo.edu organization: Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA – sequence: 3 givenname: Houping orcidid: 0000-0002-6981-8842 surname: Xiao fullname: Xiao, Houping email: houpingx@buffalo.edu organization: Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA – sequence: 4 givenname: Klara orcidid: 0000-0001-6813-3043 surname: Nahrstedt fullname: Nahrstedt, Klara email: klara@illinois.edu organization: Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA |
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| SubjectTerms | Agglomeration Data aggregation Data management Data privacy Detection Electronic devices Incentive mechanism Interactive systems mobile crowd sensing Perturbation methods Privacy privacy preservation Reliability Sensors Staff participation System effectiveness Task analysis Workers |
| Title | Incentive Mechanism for Privacy-Aware Data Aggregation in Mobile Crowd Sensing Systems |
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