A reliability and truth-aware based online digital data auction mechanism for cybersecurity in MCS

Abstract

Mobile Crowd Sensing (MCS) platform recruits participants to sense and report data, thus to build and provide services to consumers, which is a promising computing paradigm. However, malicious attacks based on false data may damage network security, and some low-trust participants may not complete tasks or submit false data since it costs resources to sense data. Both these behaviors do great harm to cybersecurity and reduce the benefit of the platform. Even if many incentive mechanisms have been proposed, there are three critical properties that are not well considered, i.e., truth-reduction, reliability-zero and time-discounting properties, which correspond to the effects of malicious attacks and the uncertain behaviors of participants. In this paper, we take these properties into account and propose a Time, Reliability and Truth-aware Online Auction (TRT-OA) mechanism to ensure cybersecurity and maximize the benefit of the platform, introducing a function T̃ and a RT coefficient to select more secure and profitable participants. We prove that TRT-OA mechanism achieves computational efficiency, budget feasibility, truthfulness, individual rationality and strategy-proofness. By comparing it with OMG and TDMC, we show that the benefit of TRT-OA mechanism increases by 44.06%.