Beyond data collection, future mobile crowdsensing (MCS) in complex applications must satisfy diverse requirements, including reliable task completion, budget and quality constraints, and fluctuating worker availability. Besides raw-data and location privacy, workers' intent/preference traces can be exploited by an honest-but-curious platform, enabling intent inference from repeated observations and frequency profiling. Meanwhile, worker dropouts and execution uncertainty may cause coverage instability and redundant sensing, while repeated global online re-optimization incurs high interaction overhead and enlarges the observable attack surface. To address these issues, we propose iParts, an intent-preserving and risk-controllable two-stage service provisioning framework for dynamic MCS. In the offline stage, workers report perturbed intent vectors via personalized local differential privacy with memorization/permanent randomization, suppressing frequency-based inference while preserving decision utility. Using only perturbed intents, the platform builds a redundancy-aware quality model and performs risk-aware pre-planning under budget, individual rationality, quality-failure risk, and intent-mismatch risk constraints. We formulate offline pre-planning as an exact potential game with expected social welfare as the potential function, ensuring a constrained pure-strategy Nash equilibrium and finite-step convergence under asynchronous feasible improvements. In the online stage, when runtime dynamics cause quality deficits, a temporary-recruitment potential game over idle/standby workers enables lightweight remediation with bounded interaction rounds and low observability. Experiments show that iParts achieves a favorable privacy-utility-efficiency trade-off, improving welfare and task completion while reducing redundancy and communication overhead compared with representative baselines.

翻译：未来复杂应用中的移动群智感知（MCS）不仅需要收集数据，还必须满足多样化的需求，包括任务可靠完成、预算与质量约束，以及工人可用性的动态波动。除原始数据和位置隐私外，工人的意图/偏好痕迹可能被“诚实但好奇”的平台利用，通过重复观测和频率分析推断其意图。同时，工人退出与执行不确定性可能导致覆盖不稳定及冗余感知，而反复的全局在线重优化会带来高昂的交互开销，并扩大可观测的攻击面。为解决上述问题，我们提出iParts——一种面向动态MCS的意图保持与风险可控的两阶段服务提供框架。离线阶段，工人通过个性化本地差分隐私结合记忆/永久随机化机制上报扰动后的意图向量，在抑制基于频率推断的同时保留决策效用。平台仅利用扰动意图构建冗余感知质量模型，并在预算、个体理性、质量失效风险及意图失配风险约束下进行风险感知预规划。我们将离线预规划建模为精确势博弈，以期望社会福利作为势函数，确保受约束的纯策略纳什均衡及异步可行改进下的有限步收敛。在线阶段，当运行时动态导致质量缺陷时，基于空闲/待命工人构建的临时招募势博弈能够以有限交互轮次和低可观测性实现轻量级修复。实验表明，iParts实现了优越的隐私-效用-效率权衡，相较于代表性基线方法，在提升社会福利与任务完成率的同时降低了冗余和通信开销。