Incentive Mechanism for Uncertain Tasks under Differential Privacy

TL;DR

This paper introduces HERALD*, an incentive mechanism for mobile crowd sensing that ensures privacy and truthfulness in uncertain, dynamic environments, addressing gaps in existing static-task-focused approaches.

Contribution

HERALD* is a novel incentive mechanism that incorporates uncertainty and hidden bids, providing privacy and truthfulness guarantees in dynamic mobile crowd sensing environments.

Findings

HERALD* satisfies truthfulness, individual rationality, and differential privacy.

The mechanism demonstrates low computational complexity.

Evaluations confirm reduced social cost and effective privacy protection.

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.