Masking Intent, Sustaining Equilibrium: Risk-Aware Potential Game-empowered Two-Stage Mobile Crowdsensing

2026-03-19

AI Summary

This work addresses key challenges in mobile crowdsensing, including intent privacy leakage, coverage instability due to worker dropout, high overhead from global re-optimization, and an expanded attack surface. To tackle these issues, the authors propose iParts, a novel framework that uniquely integrates intent privacy preservation with redundant sensing under risk-aware constraints. In the offline phase, iParts performs risk-aware pre-planning by perturbing user intents using personalized local differential privacy; in the online phase, it employs a lightweight asynchronous feasible improvement algorithm to enable dynamic repair with minimal interaction and observability. The framework is grounded in a risk-aware game-theoretic model equipped with an exact potential function, ensuring Nash equilibrium existence and convergence. Experimental results demonstrate that iParts significantly improves social welfare and task completion rates while reducing redundant sensing and communication overhead, outperforming existing baseline approaches—all without compromising intent privacy.

Technology Category

• Machine Learning: Privacy
• Humans and AI: Crowd Sourcing and Human Computation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

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.

Problem

• mobile crowdsensing
• intent privacy
• worker dropout
• coverage instability
• interaction overhead

Innovation

• intent privacy
• potential game
• risk-aware planning
• local differential privacy
• two-stage mobile crowdsensing