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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

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.

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

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.
Paper Structure (46 sections, 11 theorems, 75 equations, 6 figures, 3 tables, 4 algorithms)

This paper contains 46 sections, 11 theorems, 75 equations, 6 figures, 3 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Preliminaries and System Model
  4. Overview of Our Methodology
  5. Basic Modeling
  6. Intent-Report Distortion Loss Model
  7. Attack Model on Intent Privacy
  8. One-snapshot attack
  9. Multi-snapshot attack
  10. Personalized LDP Guarantee
  11. Offline Stage: Potential Game-empowered Intent-Private and Risk-Aware Pre-Plan
  12. Redundancy-Aware Sensing Quality
  13. Utilities and Risks of Workers and Tasks
  14. Joint Optimization in the Offline Stage
  15. Design of Module A
  16. ...and 31 more sections

Key Result

Theorem 1

Given $\mathcal{A}^{\mathrm{feas}}$, game $\mathcal{G}^{\mathrm{off}}$ is an exact potential game with potential function Hence, the game admits at least one pure-strategy constrained NE and satisfies the FIPzhu2022nash.

Figures (6)

  • Figure 1: Framework and procedure in terms of a timeline associated with our proposed iParts in dynamic MCS.
  • Figure 2: A flow chart regarding our proposed RAPCoD in the offline stage.
  • Figure 3: Performance comparison in terms of SW, TU, WU and TCR, where (a)--(c) set the number of tasks to 60.
  • Figure 4: Performance comparisons in terms of interaction overhead.
  • Figure 5: Performance comparisons in terms of OeIE, OSR, MFL and MSR, which consider 40 tasks and 160 workers.
  • ...and 1 more figures

Theorems & Definitions (28)

  • Definition 1: Expected intent-report distortion (EIRD)
  • Definition 2: $\varepsilon_j$-personalized LDPyu2017dynamic
  • Definition 3: Constrained NE
  • Definition 4: Exact potential game
  • Theorem 1: Potential structure and existence of NE
  • proof
  • Definition 5: Constrained NE (online stage)
  • Definition 6: Exact potential game
  • Theorem 2: Exact potential property and equilibrium existence
  • proof
  • ...and 18 more