Ranking a given set of items is a fundamental problem with widespread applications in information retrieval, recommendation systems, and beyond. Crowdsensing data trading (CDT) systems provide an effective means to collect opinions from distributed workers for ranking tasks. However, these systems face several challenges, including preserving privacy, maintaining ranking accuracy, optimizing worker participation, and ensuring incentive compatibility for all stakeholders. To address these challenges, we propose OPAR-IC, an integrated framework designed to enhance efficiency and privacy in CDT systems. The proposed framework integrates a multi-armed bandit (MAB) based approach to dynamically adjust ranking granularity, a hybrid privacy-preserving mechanism combining logarithmic and binary methods to safeguard worker data, and a combinatorial MAB model for efficient worker recruitment. To enhance worker participation and ensure incentive compatibility among all stakeholders, we integrate a hierarchical Stackelberg game into the framework, balancing competing incentives and achieving equilibrium. Our approach is thoroughly validated through theoretical analysis, offering theoretical guarantees for privacy, ranking accuracy, worker participation, and incentive compatibility. Extensive experiments on real-world datasets demonstrate significant improvements in adaptive ranking accuracy, privacy preservation, worker recruitment, and three-party incentive compatibility.