Achieving Efficient and Secure Task Allocation Scheme in Mobile Crowd Sensing

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI:10.1109/CSCloud-EdgeCom58631.2023.00022

Zhixue Li, Shiwen Zhang, N. Xiong, Wei Liang

{"title":"Achieving Efficient and Secure Task Allocation Scheme in Mobile Crowd Sensing","authors":"Zhixue Li, Shiwen Zhang, N. Xiong, Wei Liang","doi":"10.1109/CSCloud-EdgeCom58631.2023.00022","DOIUrl":null,"url":null,"abstract":"In recent years, as a novel perceptual paradigm, Mobile Crowd Sensing (MCS) has gradually become one of the most popular research contents. It utilizes mobile devices carried by users to collect various sensing data about social events and phenomena. To improve the credibility of the data, it is critical to recruit mobile users, but it leads to the privacy leakage of mobile users. Therefore, how to achieve efficient task allocation while protecting user data privacy is a challenging problem in MCS. In this paper, we propose an efficient and secure task allocation scheme (ESTA). In ESTA, the service provider enables to forecast the spatial distribution of sensing users and select high quality sensing data according to their trust levels without invading user privacy. By utilizing the advantage of federated learning (FL) that does not centrally collect the user data to prevent privacy leakage. Finally, we show the security properties of ESTA and demonstrate its efficiency in terms of task finished ratio and task allocation ratio.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"11 1","pages":"78-84"},"PeriodicalIF":3.7000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cloud Computing-Advances Systems and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00022","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

In recent years, as a novel perceptual paradigm, Mobile Crowd Sensing (MCS) has gradually become one of the most popular research contents. It utilizes mobile devices carried by users to collect various sensing data about social events and phenomena. To improve the credibility of the data, it is critical to recruit mobile users, but it leads to the privacy leakage of mobile users. Therefore, how to achieve efficient task allocation while protecting user data privacy is a challenging problem in MCS. In this paper, we propose an efficient and secure task allocation scheme (ESTA). In ESTA, the service provider enables to forecast the spatial distribution of sensing users and select high quality sensing data according to their trust levels without invading user privacy. By utilizing the advantage of federated learning (FL) that does not centrally collect the user data to prevent privacy leakage. Finally, we show the security properties of ESTA and demonstrate its efficiency in terms of task finished ratio and task allocation ratio.

查看原文本刊更多论文

在移动人群感知中实现高效安全的任务分配方案

近年来，移动人群感知作为一种全新的感知范式逐渐成为最热门的研究内容之一。它利用用户随身携带的移动设备，收集有关社会事件和现象的各种传感数据。为了提高数据的可信度，招募移动用户至关重要，但这会导致移动用户的隐私泄露。因此，如何在保护用户数据隐私的同时实现高效的任务分配是MCS中一个具有挑战性的问题。本文提出了一种高效安全的任务分配方案(ESTA)。在ESTA中，服务提供商可以在不侵犯用户隐私的情况下，预测感知用户的空间分布，并根据用户的信任程度选择高质量的感知数据。通过利用联邦学习(FL)不集中收集用户数据的优势，防止隐私泄露。最后，我们展示了ESTA的安全特性，并从任务完成率和任务分配率两方面证明了它的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Cloud Computing-Advances Systems and Applications Computer Science-Computer Networks and Communications

CiteScore

6.80

自引率

7.50%

发文量

审稿时长

75 days

期刊介绍： The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. Comprehensive review and survey articles that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.