群智感知中支持隐私保护的激励机制研究  

Research on Incentive Mechanism with Privacy-Preserving in Mobile Crowd Sensing

作　　者：梁艳[1,2] 安健[2,3] 胡先智[4] 杨倩 司海峰[1] LIANG Yan;AN Jian;HU Xian-Zhi;YANG Qian;SI Hai-Feng(School of Technology,Xi’an Siyuan University,Xi’an 710038;School of Computer Science and Technology,Xi’an Jiaotong University,Xi’an 710049;Shaanxi Province Key Laboratory of Computer Network,Xi’an Jiaotong University,Xi’an 710049;Center of Network Information&Management,Xi’an University of Technology,Xi’an 710048)

机构地区：[1]西安思源学院理工学院,西安710038 [2]西安交通大学计算机科学与技术学院,西安710049 [3]西安交通大学陕西省计算机网络重点实验室,西安710049 [4]西安理工大学网络信息管理中心,西安710048

出　　处：《计算机学报》2020年第12期2414-2432,共19页Chinese Journal of Computers

基　　金：国家重点研发计划课题(2018YFB1800304);国家自然科学基金(61502380);陕西省重点研发项目(2020GY-033,2019GY-005,2017ZDXM-GY-011);陕西省教育厅专项科学研究计划项目(19JK0686)资助.

摘　　要：针对已有大多数研究在设计激励机制时未考虑用户的隐私泄露问题,本文提出一种支持隐私保护的激励机制综合方案IMPP(Incentive Mechanism with Privacy-Preserving in mobile crowd sensing).首先,基于轻量级隐私保护思想,采用单向安全哈希函数生成256位哈希值作为参与者的匿名身份标识,以此来保护参与者的身份隐私;其次,依据参与者的数据效用值、期望回报及感知任务预算实现面向数据质量的补偿激励,选择性价比最高的胜出者;接着,借助分布式压缩感知理论,对胜出者的原始感知数据压缩处理,得到剔除冗余的观测值,并在观测值中添加哈希函数值等噪扰数据后传送于服务器端聚合,以增强感知数据的隐私性保护,之后对隐私数据集进行完整性校验并重构;最后,利用真实数据集,通过仿真实验对IMPP的有效性进行对比分析.实验结果表明,IMPP机制在隐私保护水平、数据完整性、数据精确性、时间效率、评估准确率、重构匹配度及激励效果等方面是高效的.Mobile crowd sensing(MCS)applications are becoming more and more widespread with the popularity of mobile smart devices.And it is a current research hotspot.MCS uses the mobile terminal equipment of ordinary users to carry out conscious or unconscious collaboration through the mobile Internet,so as to realize sensing tasks distribution and sensed data collection,and complete large-scale social sensing tasks.Accordingly,MCS needs a large number of users to participate in sensing tasks,and hopes that users can collect high-quality sensed data and provide better service quality to the public.However,during the user performing sensing tasks,the cost is relatively high because it not only consumes the user’s time,but also consumes the user’s equipment resources.In addition,sensed data collected by the user may imply sensitive information related to the user,e.g.,identity information,living habits,health status and social relations,which results in the user to face the risk of personal privacy leakage.But privacy leakage will hinder the user’enthusiasm to participate.Therefore,we studied the comprehensive mechanism of privacy-preserving and compensation incentive to MCS.On the one hand,it can reduce users’concerns about their own privacy leakage,and on the other hand,it can compensate users for the cost of participating in sensing tasks,so as to mobilize the enthusiasm of users and attract more users to participate in the sensing tasks for a long time.It is of great significance to further expand MCS application.In the existing research,the research on the comprehensive scheme or the integrated system framework that combines privacy-preserving and incentive mechanism is still a minority.Many studies usually focus on the user’s unilateral privacy-preserving or compensation incentive,and these studies don’t fully consider the communication consumption caused by the transmission of a large number of redundant sensed data,and the risk of the user’s privacy leakage under the incomplete trusted sensing platform

关 键 词：群智感知 隐私保护 激励机制 哈希函数 分布式压缩感知 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]