Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Abstract

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users' data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user's privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners' dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support

“…Liu et al [63] propose a hybrid incentive mechanism based on reputation and rewards, with an encryption algorithm scheme for privacy protection. While there are different types of incentives for MCS, such as monetary incentives, virtual currencies, gamification and social interactions, more recent approaches propose a hybrid incentive strategy [64].…”

Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing

“…Existing solutions can be basically divided into two categories: oblivious transfer scheme [27] and encoding and homomorphic encryption scheme [28]. Among them, encoding and homomorphic encryption scheme is often used to solve the problem of multiparticipant collaborative transmission of data [29,30], and there are many studies to improve the encryption or encoding [31,32]. It can allow participants to use encrypted or encoded data to calculate, and finally obtain the calculation results, but cannot know the input data of each participant.…”

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

“…Existing studies mostly employ single-attribute incentive mechanisms. Although some hybrid incentive mechanisms have been proposed for crowdsensing [51,52], there are still bottleneck problems in usability due to the difficulty of hybrid data management and the need to adjust weightings under a hybrid incentive mechanism. Different from existing incentive mechanisms, our hybrid mechanism is based on consortium blockchain, which has better openness and flexibility for requesters and workers.…”