电子学报2025，Vol.53Issue(3)：878-894,17.DOI:10.12263/DZXB.20240938
满足地理不可区分性的偏好感知多对多任务分配算法
A Preference-aware Many-to-Many TAsk Allocation Algorithm Under Geo-Indistinguishability
摘要
Abstract
In spatial crowdsourcing,task allocation is a crucial prerequisite for subsequent location-aware data collec-tion.To tackle potential location privacy breaches,researchers often adopt geo-indistinguishability.Existing approaches that satisfy Geo-I are often designed for one-to-one scenarios,while implicitly assume that workers can perform any task,and they often focus on minimizing the average travel distance,rather than maximizing the number of task allocation.Further-more,these studies often incorporate the planar laplacian mechanism to achieve Geo-I.However,due to the randomness and unbounded nature of PL,it can result in excessive noise in the location data uploaded by workers,significantly deteriorating the utility of task allocation.This can lead to either long distances or unassigned tasks.To address these problems,we pro-pose MONITOR(Many-to-many task allOcation under geo-iNdIsTinguishability for spatial crOwdsouRcing),a new priva-cy-preserving task allocation approach for many-to-many scenario.The general idea of MONITOR is to upload the distanc-es from each worker's true location to the obfuscated preferred tasks'locations instead of uploading each obfuscated work-er's location.In MONITOR,to collect the distances for subsequent task allocation,we design an obfuscated distance collec-tion method,called GroCol(Group-based obfuscated distance Collection).To improve the utility for task allocation,we de-velop a parameter independent obfuscated distance comparison method called ParCom(Parameter-free obfuscated distance Comparison).To illustrate the effectiveness of MONITOR,we first theoretically analyze its privacy guarantee,task utility,and computational complexity.We then empirically show on two real-world datasets and one synthetic dataset that MONI-TOR share similar results to that of non-private task allocation about the number of assigned tasks,and reduce the average travel distance by more than 20%compared to the baseline approaches.关键词
空间众包/任务分配/隐私保护/地理不可区分性/平均旅行距离Key words
spatial crowdsourcing/task allocation/privacy protection/geo-indistinguishability/average travel dis-tance分类
计算机与自动化引用本文复制引用
张朋飞,翟睿辰,程祥,张治坤,刘西蒙,王杰..满足地理不可区分性的偏好感知多对多任务分配算法[J].电子学报,2025,53(3):878-894,17.基金项目
安徽高校自然科学研究项目(No.2024AH050364) Natural Science Research Project of Anhui Educational Committee(No.2024AH050364) （No.2024AH050364）
