Invisible Hand: A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models

Privacy issues are strongly impeding the development of mobile crowd sensing (MCS) applications. Under the current MCS framework, processes including bidding, task assignment, and sensed data uploading are all potentially risky for participants. As an effort toward this issue, we propose a framework that enhances the location privacy of MCS applications by reducing the bidding and assignment steps in the MCS cycle. Meanwhile, to reduce the unnecessary privacy loss while maintaining the required quality of service (QoS), economic theory is used to help both the service provider and participants to decide their strategies. We propose schemes based on both the Monopoly and Oligopoly models. In the former case, the participants cooperate to gain exclusive control of the supply of crowd sensing data, while the latter case is a state of limited competition. The parameters in different schemes are analyzed, and the strengths and weaknesses of both schemes are discussed. Additionally, the proposed schemes are evaluated by extensive simulations, and the results are discussed in detail.

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