An Efficient Anonymous Reputation System for Crowd Sensing Recently, crowd sensing has been intensively researched, due to the rapid growth of sensorintegrated mobile devices. Crowd sensing is a participatory sensing service where a server gathers and analyzes sensing data submitted from mobile devices of lots of users. In crowd sensing, the useru0027s anonymity is desired, since the server gathers sensitive data from the participants including their GPS locations and moving path. However, the anonymous data submission may compromise the trust of the sensing data, because anonymous users may submit inappropriate sensing data, but they cannot be traced. Therefore, as the system to achieve both anonymity and trust in crowd sensing, ARTSense has been proposed. In the system, the trust of the sensing data is assessed on the sensed environment, other usersu0027 sensing, and the reputation of the user, and furthermore the reputation of the user is anonymously managed on the feedback from the trust assessment for the data. However, the anonymous reputation system of ARTSense has the efficiency problem, i.e., the user needs to wait a random time after the data submission phase before requesting the reputation update, which causes the communication delay. In this paper, we propose an efficient anonymous reputation system for crowd sensing, which can be integrated to the trust assessment in ARTSense. In the proposed system, during the data submission, the reputation update is anonymously completed. This is because the server does not manage the reputation of each user, but each user manages hisher reputation in the user side, where the the validity of the reputation is ensured by a certificate and anonymously checked by zeroknowledge proofs. Therefore, the proposed system achieves the better efficiency with no delay.

Trustworthiness and ComfortAware Participant Recruitment for Mobile CrowdSensing in Smart Environments Mobile crowdsensing (MCS) has gained significant momentum in recent years for sensory data acquisition through nondedicated sensors. Even though most of the participants are assumed to be willing to participate in the sensing campaigns, some smartphone users may be reluctant to grant access to particular sensors in their devices. Besides this, the presence of adversaries in the participant pool makes the participant selection problem further challenging. With these in mind, we introduce Reputation and Comfort Level Aware Participant Selection (RACLAPS)which allows participants to modify their available set of sensors that can be accessed by central platformtask publisher i.e. participants can turnoff sensors according to their comfort. To demonstrate the effect of RACLAPS, we utilize Selective and Reputationaware Recruitment (SRR) in which participants are selective in choosing their task to sense solely to improve income. To enrich the discussion, NonSelective and Reputationaware Recruitment (NSR) is also considered in which participants are given no choice regarding selectiveness, sensor configuration. Simulation results show that RACLAPS improves average user utility by 7.6% compared to its predecessor, SRR. We also mention that average discomfort per participant is reduced by 6.4% under RACLAPS when compared to nonselective and selective recruitment approaches.