Privacy-respecting auctions and rewarding mechanisms in mobile crowd-sensing applications

Abstract

Mobile Crowdsensing (MCS) has emerged as a new paradigm for data collection and knowledge representation, where people use their devices to interact with the environment and create a more accurate picture of their surroundings. In many MCS scenarios it is desirable to give micro-payments to contributors as an incentive for their participation. However, to further encourage participants to use the system, one important requirement is protection of user privacy. In this work we present a multi-attribute reverse auction mechanism as an efficient way to offer incentives to users by allowing them to determine their own price for the data they provide, but also as a way to motivate them to submit better quality data. Our auction protocol guarantees bidders' anonymity and suggests a new rewarding mechanism that enables winners to claim their reward without being linked to the data they contributed. We have analyzed our protocol and showed that it offers strong security and privacy guarantees in all phases of the auction process, from bidding to rewarding. Additionally, we have implemented the protocol using off-the-shelf cryptographic primitives; our experiments show that the protocol is scalable, it can be applied to a large class of auctions and remains efficient from both a computation and communication point of view so that it can be run to the users' mobile devices.