基于差分隐私模型的移动众包系统路径隐私研究  

Research on Path Privacy Protection of Mobile Crowdsourcing System in Swarm Intelligence Aware Networks

作　　者：沈德松 SHEN Desong(School of Business,Anhui Wenda University of Information Engineering,Hefei 231201,China)

机构地区：[1]安徽文达信息工程学院商贸学院,安徽合肥231201

出　　处：《黄山学院学报》2024年第3期23-26,共4页Journal of Huangshan University

摘　　要：众包的概念自被提出以来,就引起了社会各界的关注。在大数据时代背景下智能控制的迅速发展也为众包任务的执行提供了有利条件。众包具有方便、快速、有效的优势,这必然成为许多学者的重点研究方向。然而为了避免出现一些安全隐患,在整个众包任务的执行过程中参与者想要尽可能地提供更少的个人信息,在确保隐私数据安全地同时能顺利地执行众包任务是设计众包机制首要考虑的问题。因此,对众包以及其工作流程、差分隐私的数学定义、实现机制进行阐述,再将差分隐私应用在不同众包场景下进行分析,最后将基于保序回归的差分隐私直方图构造算法应用到真实数据集上。通过对比分析发现此算法构造的直方图数据精度更高。Crowdsourcing has attracted attention from all sectors of society since it was proposed.The rapid development of intelligent control in the era of big data also provides favorable conditions for the execution of crowdsourcing tasks.Crowdsourcing tasks can be completed more quickly and effectively.These advantages also determine that crowdsourcing will become the research focus of many scholars inevitably.However,in order to avoid some security risks,participants expect to provide as little personal information as possible during the whole execution process of the crowdsourcing task.Therefore,ensuring the security of private data while performing crowdsourcing tasks smoothly is the primary consideration in the design of crowdsourcing mechanism.Therefore,this paper firstly describes crowdsourcing and its workflow,mathematical definition and implementation mechanism of differential privacy.Secondly,it analyzes the application of differential privacy in different crowdsourcing scenarios.Finally,the differential privacy histogram construction algorithm based on order preserving regression is applied to real data sets.Through comparative analysis,the final experimental results show that the histogram data constructed by this algorithm has higher accuracy.

关 键 词：众包 隐私保护 差分隐私 保序回归 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]