ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (7): 1359-1370.doi: 10.7544/issn1000-1239.2018.20180080

所属专题: 2018物联网安全专题

• 信息安全 • 上一篇    下一篇

移动群智感知中基于用户联盟匹配的隐私保护激励机制

熊金波1,马蓉1,牛犇2,3,郭云川2,3,林立1   

  1. 1(福建师范大学数学与信息学院 福州 350117); 2(中国科学院信息工程研究所 北京 100093); 3(信息安全国家重点实验室(中国科学院信息工程研究所) 北京 100093) (jinbo810@163.com)
  • 出版日期: 2018-07-01
  • 基金资助: 
    国家重点研发计划项目(2016YFB0800700);国家自然科学基金项目(61502489,61502103,61402109);2018年国家级大学生创新创业训练计划(创新训练类)项目(201810394008).

Privacy Protection Incentive Mechanism Based on User-Union Matching in Mobile Crowdsensing

Xiong Jinbo1, Ma Rong1, Niu Ben2,3, Guo Yunchuan2,3, Lin Li1   

  1. 1(College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117); 2(Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093); 3(State Key Laboratory of Information Security (Institute of Information Engineering, Chinese Academy of Sciences), Beijing 100093)
  • Online: 2018-07-01

摘要: 移动群智感知网络作为一种全新的物联网感知模式为实现泛在深度社会感知提供了一种全新的方式和手段.在移动群智感知网络中汇聚了大量蕴含用户敏感、隐私信息的感知数据,并能从中挖掘出大量极具应用价值的信息,这极大地增加了黑客攻击、隐私数据泄露的风险.在激励更多感知用户参与感知任务并提供真实数据的同时如何更好地保护感知数据和感知平台的隐私安全成为一个突出而紧迫的关键问题.针对上述问题,提出一种基于布隆过滤器的用户联盟匹配方案,利用布隆过滤器和二元混淆向量内积计算进行相似度估计,在用户上传感知数据之前可选择进行用户联盟匹配形成感知用户联盟,从而有效保护个人隐私信息;同时针对现有隐私数据交集计算的效率问题提出一种轻量级感知数据交集计算协议,在不泄露任一方真实数据的情况下,实现隐私数据交集运算.最后提出一种基于用户联盟匹配的信誉感知激励机制,在提高感知任务处理效率的基础上有效地控制了预算开支.安全分析表明:所提用户联盟匹配方案是可证明安全的,所提感知数据交集计算协议是安全的.性能分析和实验结果表明:所提出的信誉感知激励机制是高效的.

关键词: 移动群智感知, 联盟匹配, 隐私交集计算, 信誉激励, 隐私保护

Abstract: As a novel Internet of things (IoT) sensing mode, mobile crowdsensing provides a new way and means for ubiquitous social perception. A large number of sensing data containing sensitive and private information of sensing users is gathered in the mobile crowdsensing, and a great deal of valuable information can be mined, which greatly increases the risk of hacker attacks and private data leakage. While encouraging more sensing users to participate in sensing tasks and providing real data, how to better protect the privacy of sensing data and sensing platform has become a prominent and pressing key issue. In order to solve the above problems, this paper proposes a user-union matching scheme based on the Bloom filter. Before the sensing users upload the sensing data who can choose using the Bloom filter and the binary product of the confusion vector to estimate the similarity, and effectively protect personal privacy information. Meanwhile, aiming at the efficiency of the private set intersection of the sensing data, this study puts forward a light-weight private sensing data set intersection protocol, which can realize private sensing data intersection operation without leakage of any user’s real sensing data. Furthermore, we propose a reputation-aware incentive mechanism based on user-union matching, which can effectively control the budget expenditure on the basis of improving the processing efficiency of sensing tasks. Finally, the security analysis shows that the proposed user-union matching scheme is provably secure, and the proposed private sensing data set intersection protocol is secure, and the performance analysis and experimental results show that the proposed reputation-aware incentive mechanism is efficient and effective.

Key words: mobile crowdsensing, union matching, private set intersection, reputation incentive, privacy protection

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