Budget-constrained Worker Recruitment Algorithm Based on Local Differential Privacy
HUANG Shanshan
FANG Xianjin
Abstract：To address the challenge of balancing budget constraints and location privacy in mobile crowdsensing(MCS),a budget-constrained worker recruitment algorithm based on local differential privacy(LBWR)was proposed.Firstly,an adaptive circular perturbation(ACP)mechanism was designed,where perturbation centers and radii were dynamically optimized and the exponential mechanism was applied to generate perturbed locations,thereby protecting location privacy while maintaining data utility.Secondly,a quality-aware recruitment algorithm(QARA)was introduced,which employed weighted Voronoi diagrams to capture regional characteristics and utilized ant colony optimization to select worker sets under budget limitations,enabling coverage-quality-driven recruitment.Finally,experiments on the Gowalla location-based social network dataset(Gowalla)and driving directions based on taxi trajectories(T-drive)in Beijing datasets showed that the LBWR's consistently outperformed comparison algorithms in coverage quality,root mean squared error,and minimum cost maximum workload.Specifically,LBWR achieved superior performance in coverage quality,ACP effectively reduced the root mean squared error,and QARA outperformed comparison algorithms in both coverage quality and minimum cost maximum workload,demonstrating the LBWR's balanced capability between privacy protection and efficient worker recruitment.The LBWR effectively preserved worker privacy while ensuring efficient coverage allocation,highlighting its robustness and scalability.
Keywords：mobile crowdsensinglocal differential privacyworker recruitmentant colony optimization algorithmweighted Voronoi diagram
Publication Date：2026-03-20
Online Publishing Date：2026-03-26(First online date of this platform, not the publication date of the document)
Pages：9( 49-56,68 )