Masking Intent, Sustaining Equilibrium: Risk-Aware Potential-Game-Based Service Provision in Dynamic Mobile Crowdsensing

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 $\mathit{\text{iParts}}$, 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.