Privacy-Preserving Crowdsourced Spectrum Sensing

Dynamic spectrum access is promising for mitigating worldwide wireless spectrum shortage. Crowdsourced spectrum sensing (CSS) refers to recruiting ubiquitous mobile users to perform real-time spectrum sensing at specified locations and has great potential in mitigating the drawbacks of current spectrum database operations. Without strong incentives and location privacy protection in place, however, mobile users will be reluctant to act as mobile crowdsourcing workers for spectrum-sensing tasks. In this paper, we first formulate participant selection in CSS systems as a reverse auction problem, in which each participant's true cost for spectrum sensing is closely tied to his current location. Then, we demonstrate how the location privacy of CSS participants can be easily breached under the framework. Finally, we present PriCSS, a novel framework for a CSS service provider to select CSS participants in a differentially privacy-preserving manner. In this framework, we propose PriCSS
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and PriCSS
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, two different schemes under distinct design objectives and assumptions. PriCSS
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is an approximately truthful scheme that achieves differential location privacy and an approximate minimum payment, while PriCSS
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is a truthful scheme that achieves differential location privacy and an approximate minimum social cost. The detailed theoretical analysis and simulation studies are performed to demonstrate the efficacy of both schemes.