边云协同群智感知中隐私增强多任务分配机制

doi:10.19678/j.issn.1000-3428.0065310

摘要/Abstract

摘要： 在移动群智感知中现有研究普遍基于边缘服务器或云服务器是可信的这一前提假设，无法在提高感知数据质量的同时有效保护参与者隐私。提出一种基于半可信执行环境的隐私增强多任务分配（PEMTA）机制，基于Hilbert曲线特性对任务进行位置聚类，将相邻边缘服务器结合Paillier加密体系的同态特性进行相互协作，根据参与者和任务的匹配度为每个任务挑选最佳参与者集合，完成感知任务且不泄露参与者隐私。设计贪心冲突排除算法，根据任务佣金对冲突任务进行等级划分，按照划分后的任务等级依次为冲突任务挑选最佳的替换参与者，解决了多任务分配产生的参与者匹配冲突问题。利用动态信誉值更新算法，通过量化参与者提交的感知数据与聚合后数据的偏差，动态更新参与者的信誉值，缓解了恶意攻击造成的数据质量损失。实验结果表明，PEMTA机制具有良好的抗恶意攻击性能，感知数据质量和任务完成率相比于同类多任务分配机制平均提升了18.14%和15.47%。

关键词: 移动群智感知, 边缘计算, 多任务分配, Hilbert曲线, Paillier加密

Abstract: Studies on Mobile Crowdsensing(MCS) are generally based on the premise that the Edge Server(ES) or Cloud Server(CS) is trusted, which can not effectively protect the privacy of participants while improving the perceived data quality. A Privacy-Enhanced Multi-Task Assignment(PEMTA) mechanism based on a semi-trusted execution environment is proposed. The tasks are clustered based on Hilbert curve characteristics. The adjacent ESs are combined with the homomorphic characteristics of the Paillier encryption system for cooperation. The best set of participants is selected for each task based on the matching degree of participants and tasks to complete the perception task without disclosing the privacy of participants. The greedy conflict elimination algorithm is designed, and the conflict tasks are graded according to the task commission. The best replacement participants are selected for the conflict tasks based on the divided task level to resolve the matching participant conflict caused by multi-task allocation. The dynamic reputation value update algorithm is used to dynamically update the reputation value of participants by quantifying the deviation between the perception data submitted by participants and the aggregated data, alleviating data quality loss caused by malicious attacks. The experimental results show that the PEMTA mechanism performs satisfactorily in anti-malicious attacks. The perceived data quality and task completion rate increase by 18.14% and 15.47%, respectively, compared with similar multi-task allocation mechanisms on average.

Key words: Mobile Crowdsensing(MCS), edge computing, multi-task assignment, Hilbert curve, Paillier encryption

中图分类号:

TP393

王辉, 张玉豪, 申自浩, 刘沛骞, 蔡尚卿, 刘琨. 边云协同群智感知中隐私增强多任务分配机制[J]. 计算机工程, 2023, 49(4): 52-60.

WANG Hui, ZHANG Yuhao, SHEN Zihao, LIU Peiqian, CAI Shangqing, LIU Kun. Privacy-Enhanced Multi-Task Allocation Mechanism in Edge Cloud Collaborative Crowdsening[J]. Computer Engineering, 2023, 49(4): 52-60.

http://www.ecice06.com/CN/Y2023/V49/I4/52

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