Affiliations: [a]
School of Management, Hefei University of Technology, Hefei, China
| [b]
North Minzu University, Yinchuan, China
| [c] Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China
Correspondence:
[*]
Corresponding author. Zhiwei Ni. E-mail: [email protected].
Abstract: A framework for spatial crowdsourcing task allocation based on centralized differential privacy is proposed for addressing the problem of worker’s location privacy leakage. Firstly, by combining two stages of differential privacy noise addition and clustering matching, a spatial crowdsourcing worker dataset with high differential privacy protection can be obtained; Secondly, the dynamic problem of spatial crowdsourcing task allocation is transformed into a static combinatorial optimization problem by dividing the spatiotemporal units and the “delay matching” strategy; Finally, the improved discrete glowworm swarm optimization algorithm is used to calculate the results of spatial crowdsourcing task allocation. It has been demonstrated that, compared to the direct differential privacy noise-adding assignment method and the discrete glowworm swarm optimization assignment method, the proposed method achieves better task assignment results, with the total travel distance reduced by 12.42% and 3.56%, respectively, and the task assignment success rate increased by 11.75% and 3.34%, respectively.
Keywords: Differential privacy, k-means clustering, space crowdsourcing, task allocation, the glowworm swarm optimization algorithm
