Privacy-preserving verifiable incentive mechanism for online crowdsourcing markets
Jiajun Sun, Huadóng Ma · 2014
Mobile crowdsourcing is a new paradigm which leverages pervasive smartphones to efficiently collect and upload data, enabling numerous novel applications. Recently, a class of new mechanisms have been proposed to determine near-optimal prices of sensing tasks for online crowdsourcing markets, where users arrive online and the crowdsourcer has budget constraints. In particular, the mechanisms can motivate extensive users to participate in online crowdsourcing markets. Although it is so promising in real-life environments, there still exist many security and privacy challenges. In this paper, we present a heterogeneous-user based privacy-preserving verifiable incentive mechanism for online crowdsourcing markets with the budget constraint, not only to explore how to protect the privacy of the bids, selection preferences, and identity from participants, but also to make the verifiable payment between the crowdsourcer (the crowdsourcing organizer) and online sequential arrival users. Results indicate that our privacy-preserving verifiable mechanisms achieve the same results as the generic one without privacy preservation.