CTDST: A cautious trust-based scheme for reliable data and service trading in MCS

Mobile Crowdsensing (MCS) has become a promising paradigm for large-scale data collection and service provisioning, where workers generate sensing data, Data and Service Platforms (DSPs) process the data into services, and users consume these services. However, ensuring reliable data and service trading in such a decentralized and open environment remains challenging because malicious or unreliable workers, users, or DSPs may compromise transaction outcomes and overall system efficiency. To address this challenge, we propose CTDST, a cautious trust-based scheme for reliable data and service trading in MCS, which introduces a progressive trust evaluation mechanism across workers, DSPs, and users. In CTDST, when a requester lacks sufficient trust in potential trading partners, a cautious delegation strategy is adopted, where trusted intermediaries are selected to interact with reliable DSPs on behalf of the requester. During these interactions, the requester collects trust evidence about DSPs, while the intermediaries obtain a portion of the reward, which helps align incentives and encourages consistent participation. As interactions accumulate, trust is continuously updated based on observed outcomes, allowing the requester to gradually build reliable trust assessments. Once the requester's trust score reaches a predefined threshold, direct transactions are enabled, thereby improving interaction efficiency while maintaining reliability. Theoretical analysis and extensive simulations show that CTDST improves interaction success rates, mitigates the impact of malicious participants, and achieves a balanced trade-off between reliability and efficiency. These results demonstrate that CTDST effectively enhances the overall utility of MCS systems for both workers and service consumers.