Mobile Crowdsensing (MCS) has emerged as a promising paradigm for large-scale, real-time data collection by leveraging the sensing capabilities of widely distributed mobile workers. However, its practical adoption is challenged by privacy risks and unsustainable incentive structures that inadequately compensate for workers' inherent participation costs, leading to diminished motivation, sparse task coverage, and reduced data availability. Existing approaches either provide limited and utility-degrading privacy protection or design incentive mechanisms that incur substantial costs, even under the Nash equilibrium. To bridge this gap, we propose C-PRISM (Compre hensive Privacy-preserving and Behavioral-Incentive Sustainable crowdsensing Mechanism), an integrated framework that seamlessly combines privacy-preserving techniques with behavioral economic incentive design. Specifically, C-PRISM employs ran domized matrix perturbation for fine-grained location protection and a two-phase proxy re-encryption protocol to secure task details and sensing data across evaluation, recruitment, and transmission. Building upon this secure foundation, behavioral economic incentives grounded in prospect theory, the Aronson effect, and a dual reference-point model are introduced to promote sustained worker participation at sub-Nash-equilibrium costs. Rigorous theoretical analysis validates C-PRISM's security and individual rationality. Extensive experiments on real-world datasets demonstrate that C-PRISM increases data collection efficiency by 7.42%-193.75%, improves worker retention by 2.53%-69.78%, and effectively maintains overall system utility.
Bai et al. (2026) studied this question.