Mobile crowdsensing (MCS) is evolving from basic data collection to dynamic service provisioning, where platforms must maintain task completion, budget feasibility, and sensing quality under uncertain worker availability. Beyond raw-data and location privacy, workers' long-term intent traces, such as task-selection tendencies and participation histories, can be exploited by an honest-but-curious platform to infer private preferences from one or multiple allocation snapshots. Worker dropouts and execution uncertainty further destabilize sensing coverage, while frequent global re-optimization increases interaction overhead and observable exposure. To address these issues, we propose \textit{iParts}, an intent-preserving and risk-aware two-stage service provisioning framework for dynamic MCS. In the offline stage, workers report perturbed intent vectors through personalized local differential privacy with memoized permanent randomized response, suppressing frequency-based intent inference while retaining decision utility. The platform then builds a redundancy-aware quality model and performs risk-aware pre-planning under budget, quality-risk, and intent-mismatch constraints. This offline problem is formulated as an exact potential game with expected social welfare as the potential function, guaranteeing constrained equilibrium existence and finite-step convergence under feasible improvement dynamics. In the online stage, quality deficits are repaired through bounded-round temporary recruitment from idle or standby workers, enabling feasibility-preserving adjustment with limited exposure. Experiments show that iParts improves welfare and task completion while reducing redundancy and communication overhead against representative benchmarks.

翻译：移动群智感知正从基础数据采集演进至动态服务提供，在此过程中平台需在工人可用性不确定的情况下维持任务完成度、预算可行性与感知质量。除原始数据与位置隐私外，工人的长期意图轨迹（如任务选择倾向与参与历史）可能被诚实但好奇的平台利用，通过单次或多次分配快照推断其私人偏好。工人退出与执行不确定性进一步破坏感知覆盖的稳定性，而频繁的全局重优化会增加交互开销与可观测暴露风险。为应对上述问题，我们提出iParts——一种面向动态移动群智感知的意图保留与风险感知两阶段服务提供框架。在离线阶段，工人通过具有记忆化永久随机响应的个性化本地差分隐私上报扰动意图向量，在抑制基于频率的意图推断的同时保留决策效用。随后平台构建冗余感知质量模型，并在预算、质量风险与意图失配约束下进行风险感知预规划。该离线问题被建模为精确势博弈（以期望社会福祉为势函数），在可行改进动态下保证约束均衡存在性与有限步收敛性。在线阶段通过从空闲或待命工人中进行有限轮次临时招募修复质量缺陷，实现有限暴露下的可行性保持调整。实验表明，与代表性基准相比，iParts在提升福祉与任务完成度的同时降低了冗余与通信开销。