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Incentive Mechanism for Uncertain Tasks under Differential Privacy

Xikun Jiang, Chenhao Ying, Lei Li, Boris Düdder, Haiqin Wu, Haiming Jin, Yuan Luo

TL;DR

This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints and satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost.

Abstract

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

Incentive Mechanism for Uncertain Tasks under Differential Privacy

TL;DR

This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints and satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost.

Abstract

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.
Paper Structure (23 sections, 10 theorems, 19 equations, 10 figures, 1 table, 3 algorithms)

This paper contains 23 sections, 10 theorems, 19 equations, 10 figures, 1 table, 3 algorithms.

Key Result

Theorem 1

The exponential mechanism $\mathcal{M}_E(x, u, R)$ preserves $(\epsilon, 0)$-differential privacy.

Figures (10)

  • Figure 1: A typical MCS system.
  • Figure 2: Framework of HERALD*.
  • Figure 3: (a). Expected social cost versus different numbers of workers for uncertain tasks. (b). Expected total payment versus different numbers of workers for uncertain tasks.
  • Figure 4: (a). Expected social cost versus different numbers of sensing tasks for uncertain tasks. (b). Expected total payment versus different numbers of sensing tasks for uncertain tasks.
  • Figure 5: (a). The impact of worker's cost on the expected social cost obtained by HERALD* for uncertain tasks with the liner score function. (b). The impact of worker's cost on the expected total payment obtained by HERALD* for uncertain tasks with the liner score function.
  • ...and 5 more figures

Theorems & Definitions (28)

  • Definition 1: Truthfulness
  • Definition 2: Individual Rationality
  • Definition 3: Differential Privacydwork2014algorithmic
  • Definition 4: The Exponential Mechanism dwork2014algorithmic
  • Theorem 1: dwork2014algorithmic
  • Definition 5: Competitive Ratio on Expected Social Cost
  • Definition 6
  • Example 1
  • Definition 7: Cost-effectiveness
  • Remark 1
  • ...and 18 more