Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (11): 3249-3254.DOI: 10.11772/j.issn.1001-9081.2020020236

• Cyber security • Previous Articles     Next Articles

Privacy preservation algorithm of original data in mobile crowd sensing

JIN Xin1,2, WAN Taochun1,2, LYU Chengmei1,2, WANG Chengtian1,2, CHEN Fulong1,2, ZHAO Chuanxin1,2   

  1. 1. School of Computer and Information, Anhui Normal University, Wuhu Anhui 241002, China;
    2. Anhui Provincial Key Laboratory of Network and Information Security(Anhui Normal University), Wuhu Anhui 241002, China
  • Received:2020-03-08 Revised:2020-06-08 Online:2020-11-10 Published:2020-06-24
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61402014, 61972439, 61972438, 61871412),the Natural Science Foundation of Universities of Anhui Province (KJ2019A1164), the CERNET Next Generation Internet Creative Project (NGII20170312), the Anhui Normal University Doctor Startup Fund (2018XJJ66).

移动群智感知中原始数据隐私保护算法

金鑫1,2, 王涛春1,2, 吕成梅1,2, 王成田1,2, 陈付龙1,2, 赵传信1,2   

  1. 1. 安徽师范大学 计算机与信息学院, 安徽 芜湖 241002;
    2. 网络与信息安全安徽省重点实验室(安徽师范大学), 安徽 芜湖 241002
  • 通讯作者: 王涛春(1979-),男,安徽无为人,教授,博士生导师,博士,CCF会员,主要研究方向:群智感知、隐私保护、无线传感网;wangtc@ahnu.edu.cn
  • 作者简介:金鑫(1994-),男,安徽滁州人,硕士研究生,主要研究方向:移动群智感知隐私保护;吕成梅(1995-),女,安徽池州人,硕士研究生,主要研究方向:移动群智感知隐私保护;王成田(1997-),男,安徽宣城人,硕士研究生,主要研究方向:无线射频识别、隐私保护;陈付龙(1978-),男,安徽霍邱人,教授,博士生导师,博士,CCF会员,主要研究方向:嵌入式与普适计算、信息物理融合系统;赵传信(1978-),男,安徽凤阳人,教授,博士,CCF会员,主要研究方向:智能优化算法、网络资源分配与优化
  • 基金资助:
    国家自然科学基金资助项目(61402014,61972439,61972438,61871412);安徽省教育厅高校自然科学研究重点项目(KJ2019A1164);赛尔网络下一代互联网创新项目(NGII20170312);安徽师范大学博士启动项目(2018XJJ66)。

Abstract: With the popularity of mobile smart devices, Mobile Crowd Sensing (MCS) has been widely used while facing serious privacy leaks. Focusing on the issue that the existing original data privacy protection scheme is unable to resist collusion attacks and reduce the perception data availability, a Data Privacy Protection algorithm based on Mobile Node (DPPMN) was proposed. Firstly, the node manager in DPPMN was used to establish an online node list and send it to the source node. An anonymous path for data transmission was built by the source node through the list. Then, the data was encrypted by using paillier encryption scheme, and the ciphertext was uploaded to the application server along the path. Finally, the required perception data was obtained by the server using ciphertext decryption. The data was encrypted and decrypted during transmission, making sure that the attacker was not able to wiretap the content of the perception data and trace the source of the data along the path. The DPPMN ensures that the application server can access the original data without the privacy invasion of the nodes. Theoretical analysis and experimental results show that DPPMN has higher data security with increasing appropriate communication, and can resist collusion attacks without affecting the availability of data.

Key words: Mobile Crowd Sensing (MCS), paillier encryption, privacy preservation, collusion attack, original data

摘要: 随着移动智能设备的普及,移动群智感知(MCS)得到广泛应用的同时面临着严重的隐私泄露问题。针对现有的移动群智感知中的原始数据隐私保护方案不能抵御共谋攻击,降低了感知数据可用性的情况,提出一种基于移动节点的数据隐私保护算法(DPPMN)。首先,使用DPPMN中的节点管理器建立在线节点列表并将其发送给源节点,源节点通过列表构建数据传输的匿名路径;然后,使用paillier加密方案加密数据;接着,将密文沿路径上传至应用服务器;最后,服务器解密密文得到所需的感知数据。在数据传输时使用加解密操作,确保了攻击者不能窃听感知数据的内容,且无法沿路径追溯数据的来源。DPPMN能保证应用服务器在不侵犯节点隐私的情况下访问原始数据。理论分析和实验结果表明,DPPMN在增加适当通信量的情况下,具有较高的数据安全性,可以在抵御共谋攻击的同时不影响数据的可用性。

关键词: 移动群智感知, paillier加密, 隐私保护, 共谋攻击, 原始数据

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