A Differentially Private Framework for Spatial Crowdsourcing with Historical Data Learning

TL;DR

This paper introduces R-HT, a differentially private framework for spatial crowdsourcing that leverages historical data to improve location privacy protection, task assignment success, and efficiency.

Contribution

The paper proposes a novel framework combining historical data learning with differential privacy for spatial crowdsourcing, enhancing privacy and task allocation accuracy.

Findings

Achieves stable task assignment success rate

Reduces performance overhead

Effective for dynamic crowdsourcing environments

Abstract

Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy. It requires workers to arrive at a particular location for task fulfillment. Effective protection of location privacy is essential for workers' enthusiasm and valid task assignment. However, existing SC models with differential privacy usually perturb real-time location data for both partition and data publication. Such a way may produce large perturbations to counting queries that affect assignment success rate and allocation accuracy. This paper proposes a framework (R-HT) for protecting location privacy of workers taking advantage of both real-time and historical data. We simulate locations by sampling the probability distribution learned from historical data, use them for grid partition, and then publish real-time data under this partitioning with differential privacy. This realizes that most privacy budget is allocated to the worker count of each cell and yields an improved Private Spatial Decomposition approach. Moreover, we introduce some strategies for geocast region construction, including quality scoring function and local maximum geocast radius. A series of experimental results on real-world datasets shows that R-HT attains a stable success rate of task assignment, saves performance overhead and fits for dynamic assignment on crowdsourcing platforms.