Bidding-Model Based Privacy-Preserving Verifiable Auction Mechanism for Crowd Sensing
Jiajun Sun · arXiv (Cornell University) · 2013
Crowd sensing is a new paradigm which leverages a large number of sensor-equipped mobile phones to collect sensing data. Recently, the mix of users' sequential manners and crowd sensing, sequential crowd sensing, make it practicable in a real-life environment. Although sequential crowd sensing is promising, there still exist many security and privacy challenges. In this paper, we present a bidding-model based privacy-aware incentive mechanism for sequential crowd sensing applications in MSNs, not only to exploit how to protect the bids and subtask information privacy from participants and social profile privacy, but also to make the verifiable payment between the platform and users for sequential crowd sensing applications in MSNs. Results indicate that our privacy-preserving posted pricing mechanisms achieve the same results as the generic one without privacy preservation.