文献类型：期刊文献

英文题名：Differentially Private Task Allocation Algorithm Under Preference Protection

作者：Han, Juntao[1];Cai, Shuyue[2]

第一作者：Han, Juntao

通讯作者：Han, JT[1]

机构：[1]Henan Univ Econ & Law, Sch E Commerce & Logist Management, Zhengzhou 450046, Henan, Peoples R China;[2]Zhengzhou Univ, Sch Management Engn, Zhengzhou 450001, Henan, Peoples R China

第一机构：河南财经政法大学电子商务与物流管理学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch E Commerce & Logist Management, Zhengzhou 450046, Henan, Peoples R China.|[104849]河南财经政法大学电子商务与物流管理学院;[10484]河南财经政法大学;

年份：2022

卷号：10

起止页码：33059-33068

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20221311863841);Scopus(收录号：2-s2.0-85127056309);WOS:【SCI-EXPANDED(收录号:WOS:000776128900001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61802110, and in part by the Characteristic Backbone Discipline in Henan Province's ``Modern Service Discipline Group.''

语种：英文

外文关键词：Task analysis; Privacy; Resource management; Crowdsensing; Differential privacy; Servers; Sensors; Crowdsensing; differential privacy; geo-indistinguishability; preference protection; task allocation

摘要：Mobile crowdsensing has been widely applied as a kind of perception paradigm, and task allocation is a fundamental research issue in mobile crowdsensing. Existing task allocation algorithms under differential privacy are not suitable for preference protection scenarios as they may inject too much noise. To this end, in this paper, we propose a differentially private task allocation algorithm with preference protection, referred to as SLEPT. In SLEPT, we divide privacy budget into three parts. Specifically, we first use one part of privacy budget to perturb the location of each worker. Then we use another part of privacy budget to perturb the preference information of him. In particular, to relieve the problem that perturbation may lead to that tasks will not be allocated, we propose a two-phase preference collection mechanism called TPC. Finally, we propose a task allocation sequential updating mechanism TASU using the remaining privacy budget. It aims to reduce the travel distance of workers and improve the success rate of task allocation. Theoretical analysis shows that SLEPT satisfies differential privacy. Time complexity analysis shows that it is linearly related to the number of tasks. The results on two public datasets verify the effectiveness of SLEPT. It is worth noting that although SLEPT is proposed for task allocation, its idea is also applicable to other crowdsensing scenarios, such as high-dimensional data collection.