传感云中基于边缘计算的差分数据保护方法

doi:10.13190/j.jbupt.2019-208

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (4): 48-53,75.doi: 10.13190/j.jbupt.2019-208

传感云中基于边缘计算的差分数据保护方法

梅雅欣, 沈雪微, 赵丹, 王田

华侨大学计算机科学与技术学院, 厦门 361021

收稿日期:2019-10-18 发布日期:2020-08-15
通讯作者: 王田(1982-),男,教授,E-mail:cs_tianwang@163.com. E-mail:cs_tianwang@163.com
作者简介:梅雅欣(1996-),女,硕士生.
基金资助:
国家自然科学基金面上项目（61872154）；福建省社会科学规划一般项目（FJ2018B038）

An Edge-Based Differential Method for Data Protection in Sensor-Cloud

MEI Ya-xin, SHEN Xue-wei, ZHAO Dan, WANG Tian

School of Computer Science and Technology, Huaqiao University, Xiamen 361021, China

Received:2019-10-18 Published:2020-08-15

摘要/Abstract

摘要： 为了改进存储机制，以提供一个通用的隐私保护服务，提出了一种基于边缘的数据保护模型，将无线传感器网络收集的原始数据交由边缘服务器，并利用隐私计算算法进行差分处理.少量核心数据存储在边缘服务器和本地服务器上，其他数据传输到云端存储.基于该模型，即使存储在云中的数据被泄露，原始数据也无法被恢复，数据的隐私权得以保证；采用差分存储方法，减少了发送到云端的数据，降低了通信成本和存储成本.理论分析和大量实验结果证实了该方法有效.

关键词: 隐私保护, 边缘计算, 差分处理

Abstract: To improve the storage mechanism to provide a general privacy protection service, a data protection model based on edge computing is presented. The original data collected by wireless sensor networks is differentiated by privacy computing algorithm on edge servers. A small amount of core data is stored on edge servers and local servers,while other data is transferred to the cloud for storage. Based on the model,even if the data stored in the cloud is leaked,the original data cannot be recovered,so the privacy of the data can be ensured. The differential storage method reduces the data sent to the cloud and decreases the communication cost and storage cost. Both theoretical analyses and a large number of experiments have proved the effectiveness of the proposed method.

Key words: privacy protection, edge computing, differential processing

中图分类号:

TP309.2

梅雅欣, 沈雪微, 赵丹, 王田. 传感云中基于边缘计算的差分数据保护方法[J]. 北京邮电大学学报, 2020, 43(4): 48-53,75.

MEI Ya-xin, SHEN Xue-wei, ZHAO Dan, WANG Tian. An Edge-Based Differential Method for Data Protection in Sensor-Cloud[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(4): 48-53,75.

参考文献

[1] 曾建电, 王田, 贾维嘉, 等. 传感云研究综述[J]. 计算机研究与发展, 2017, 54(5):925-939. Zeng Jiandian, Wang Tian, Jia Weijia, et al. Summary of sensor cloud research[J]. Computer Research and Development, 2017, 54(5):925-939.
[2] Wang Tian, Zeng Jiandian, Lai Yongxuan, et al. Data collection from WSNs to the cloud based on mobile fog elements[J]. Future Generation Computer Systems, 2020, 105:864-872.
[3] 梁玉珠, 沈雪微, 邱磊, 等. 物联网中基于扩展卡尔曼滤波的移动群体定位[J]. 北京邮电大学学报, 2019, 42(2):95-100. Liang Yuzhu, Shen Xuewei, Qiu Lei, et al. Extended Kalman filter for mobile groups users localization in internet of things[J]. Journal of Beijing University of Posts and Telecommunications, 2019, 42(2):95-100.
[4] Tan Jiawei, Liu Wei, Wang Tian, et al. An adaptive collection scheme-based matrix completion for data gathering in energy harvesting wireless sensor networks[J]. IEEE Access, 2019, 7:6703-6723.
[5] 王田, 李洋, 贾维嘉, 等. 传感云安全研究进展[J]. 通信学报, 2018, 39(3):35-52. Wang Tian, Li Yang, Jia Weijia, et al. Research progress of sensor cloud security[J]. Journal of Communications, 2018, 39(3):35-52.
[6] 王田, 沈雪微, 罗皓, 等. 基于雾计算的可信传感云研究进展[J]. 通信学报, 2019, 40(3):170-181. Wang Tian, Shen Xuewei, Luo Hao, et al. Research progress of trusted sensor-cloud based on fog computing[J]. Journal on Communications, 2019, 40(3):170-181.
[7] Lopez-Falcon E, Tchernykh A, Chervyakov N, et al. Adaptive encrypted cloud storage model[C]//2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. New York:IEEE Press, 2018:329-334.
[8] Chi P, Lei C. Audit-free cloud storage via deniable attribute-based encryption[J]. IEEE Transactions on Cloud Computing, 2018, 6(2):414-427.
[9] Wang Tian, Zhou Jiyuan, Chen Xinlei, et al. A three-layer privacy preserving cloud storage scheme based on computational intelligence in fog computing[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(1):3-12.
[10] Jin Xuexue, Yu Nenghai, Zhang Chi, et al. De-duplication of encrypted data with proof of ownership in cloud storage[J]. Information Security & Communications Privacy, 2013, 8(1):224-229.
[11] Wu Yulin, Jiang Z L, Wang Xuan, et al. Dynamic data operations with deduplication in privacy-preserving public auditing for secure cloud storage[C]//2017 IEEE International Conference on Computational Science and Engineering and IEEE International Conference on Embedded and Ubiquitous Computing. New York:IEEE Press, 2017:562-567.
[12] Muthurajkumar S, Vijayalakshmi M, Kannan A. An effective data storage model for cloud databases using temporal data de-duplication approach[C]//2016 Eighth International Conference on Advanced Computing. New York:IEEE Press, 2017:42-45.
[13] Zafar F, Khan A, Malik S U R, et al. A survey of cloud computing data integrity schemes:design challenges, taxonomy and future trends[J]. Computers & Security, 2017, 65:29-49.
[14] 李道全, 张玉霞, 魏艳婷. 无线传感器网络源位置隐私保护路由协议[J]. 计算机技术与发展, 2019, 29(7):87-92. Li Daoquan, Zhang Yuxia, Wei Yanting. Energy efficient query processing techniques based on particle filters in wireless sensor networks[J]. Computer Technology and Development, 2019, 29(7):87-92.
[15] Hua Junhao, Li Chunguang. Distributed variational bayesian algorithms over sensor networks[J]. IEEE Transactions on Signal Processing, 2016, 64(3):783-798.
[16] Liang Chen, Wen Fuxi, Wang Zhongmin. Distributed parameter estimation for univariate generalized Gaussian distribution over sensor networks[J]. Circuits, Systems, and Signal Processing, 2017, 36:1311-1321.
[17] Xu Guangquan, Zhang Yao, Sangaiah A K, et al. CSP-E²:an abuse-free contract signing protocol with low-storage TTP for energy efficient electronic transaction ecosystems[J]. Information Sciences, 2019, 476:505-515.
[18] Huang Mingfeng, Liu Anfeng, Xiong N N, et al. A low-latency communication scheme for mobile wireless sensor control systems[J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2019, 49(2):317-332.
[19] 张素洁, 赵怀慈. 最优聚类个数和初始聚类中心点选取算法研究[J]. 计算机应用研究, 2017, 34(6):1617-1620. Zhang Sujie, Zhao Huaici. Research on the optimal clustering number and the selection of the initial clustering center[J]. Computer Applied Research, 2017, 34(6):1617-1620.

传感云中基于边缘计算的差分数据保护方法

An Edge-Based Differential Method for Data Protection in Sensor-Cloud

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