Masking Intent, Sustaining Equilibrium: Risk-Aware Potential Game-empowered Two-Stage Mobile Crowdsensing
Houyi Qi, Minghui Liwang, Kaiwen Tan, Wenyong Wang, Sai Zou, Yiguang Hong, Xianbin Wang, Wei Ni

TL;DR
This paper introduces iParts, a risk-aware, intent-preserving framework for mobile crowdsensing that balances privacy, quality, and efficiency through game-theoretic offline and online strategies.
Contribution
It proposes a novel two-stage game-based framework that preserves worker intent privacy and optimizes service provisioning in dynamic MCS environments.
Findings
Improves task completion rates and welfare in MCS.
Reduces redundancy and communication overhead.
Balances privacy and utility effectively.
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…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Privacy, Security, and Data Protection · Opportunistic and Delay-Tolerant Networks
