基于Hash函数的属性泛化隐私保护方案

doi:10.3969/j.issn.1671-1122.2018.03.003

信息网络安全 ›› 2018, Vol. 18 ›› Issue (3): 14-25.doi: 10.3969/j.issn.1671-1122.2018.03.003

基于Hash函数的属性泛化隐私保护方案

张磊^1,², 王斌^1,², 于莉莉²()

1. 哈尔滨工程大学计算机科学与技术学院,黑龙江哈尔滨 150001
2. 佳木斯大学信息电子技术学院,黑龙江佳木斯 154007

收稿日期:2017-12-22 出版日期:2018-03-15 发布日期:2020-05-11
作者简介:
作者简介：张磊（1982—）,男,黑龙江,博士研究生,主要研究方向为隐私保护;王斌（1979—）,男,黑龙江,博士研究生,主要研究方向为机器学习、隐私保护;于莉莉（1975—）,女,黑龙江,副教授,硕士,主要研究方向为信息安全、网络安全。
基金资助:
黑龙江省自然科学基金[F2015022];黑龙江省普通本科高等学校青年创新人才培养计划[UNPYSCT-2017149,UNPYSCT-2017175]

A Hash Function-based Attribute Generalization Privacy Protection Scheme

Lei ZHANG^1,², Bin WANG^1,², Lili YU²()

1. College of Computer Science and Technology, Harbin Engineering University, Harbin Heilongjiang 150001, China
2. College of Information Science and Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China

Received:2017-12-22 Online:2018-03-15 Published:2020-05-11

摘要/Abstract

摘要：

针对用户连续查询过程中属性可被关联并获得位置隐私的问题,文章基于属性泛化要求提出了一种基于Hash函数的属性泛化方法。该方法通过由属性转化的Hash值比较寻找具有相同属性的匿名用户,一方面防止具有攻击特性的中心服务器获得用户发送的隐私信息;另一方面通过这种Hash比较简化了相似属性的寻找过程,提高了算法的执行效率。同时,为了证明中心服务器具有潜在的攻击特性,利用博弈树量化的方式证明了中心服务器的不可靠性。最后,通过安全性分析和实验验证,并将文章提出的方法与其他同类算法进行比较,进一步证明了文中算法在隐私保护能力和算法执行效率方面的优势。

关键词: 基于位置服务, 属性泛化, Hash函数, 博弈树, 隐私保护

Abstract:

In order to cope with the problem of attribute can be used as background knowledge to correlate the location privacy by the adversary, then based on the conception of attributes generalization, a Hash based attributes generalization scheme is proposed. With this scheme, attributes are transformed into a fixed Hash value, and then select anonymous users with value comparison. In the procedure of value comparison, the central server cannot get any information about the user as attributes are transformed into a fixed Hash value. Furthermore, the procedure of comparison for similar attributes finding does not need to compare each attribute but just needs to compare the Hash value, and then the performance efficiency is improved. In this paper, for the purpose of verifying the un-trusted of central servers, a game tree was given to infer and quantify the probability of attack. At last, security analysis and simulation experiment were given with other similar algorithms, and the results of verification and comparison were used to further demonstrate the superiority of our proposed scheme in the capability of privacy protection as well as the execution efficiency.

Key words: LBS, attribute generalization, Hash function, game tree, privacy protection

中图分类号:

TP309

张磊, 王斌, 于莉莉. 基于Hash函数的属性泛化隐私保护方案[J]. 信息网络安全, 2018, 18(3): 14-25.

Lei ZHANG, Bin WANG, Lili YU. A Hash Function-based Attribute Generalization Privacy Protection Scheme[J]. Netinfo Security, 2018, 18(3): 14-25.

图/表 12

图1

图2

表1

图3

图4

图5

图6

图7

图8

图9

图10

图11

参考文献 22

[1]	GRUTESER M, GRUNWALD D.Anonymous usage of location-based services through spatial and temporal cloaking[C]//ACM. 1st International Conference on Mobile Systems, Applications and Services, May 5-8, 2003, San Francisco, California. New York: ACM, 2003: 31-42.
[2]	LIU Fuyu L, HUA K A, CAI Ying.Query l-diversity in Location-based Services[C]//IEEE. 10th International Conference on Mobile Data Management: Systems, Services and Middleware, May 18-20, 2009, Taipei, China. New Jersey: IEEE, 2009: 436-442.
[3]	XIAO Zhen, XU Jianliang, MENG Xiaofeng. p-Sensitivity: A Semantic Privacy-protection Model for Location-based Services[C]//IEEE. 9th International Conference on Mobile Data Management Workshops, April 27-30, 2008, Beijing, China. New Jersey: IEEE, 2008: 47-54.
[4]	GHINITA G, KALNIS P, KHOSHGOZARAN A, et al.Private Queries in Location Based Services: Anonymizers are not Necessary[C]//ACM. 2008 ACM SIGMOD International Conference on Management of Data, June 10-12, 2008, Vancouver, Canada. New York: ACM, 2008: 121-132.
[5]	CHOW C Y, MOKBEL M F, LIU Xuan.A Peer-to-Peer Spatial Cloaking Algorithm for Anonymous Location-based Service[C]//ACM. 14th Annual ACM International Symposium on Advances in Geographic Information Systems, November 10-11, 2006, Arlington, Virginia, USA. New York: ACM, 2006: 171-178.
[6]	SCHLEGEL R, CHOW C Y, HUANG Qiong, et al.User-defined Privacy Grid System for Continuous Location-based Services[J]. IEEE Transactions on Mobile Computing, 2015, 14(10): 2158-2172.
[7]	NIU Ben, ZHU Xioayan, LI Qinghua, et al.A Novel Attack to Spatial Cloaking Schemes in Location-based Services[J]. Future Generation Computer Systems, 2015, 49(C): 125-132.
[8]	MA Chunguang, ZHANG Lei, YANG Songtao, et al.Achieve Personalized Anonymity through Query Blocks Exchanging[J]. China Communications, 2016, 13(11): 106-118.
[9]	SUN Yanming, CHEN Min, HU Long, et al. ASA: Against Statistical Attacks for Privacy-aware users in Location Based Service[EB/OL]. , 2017-10-12.
[10]	马春光,张磊,杨松涛. 位置轨迹隐私保护综述[J]. 信息网络安全,2015(10):24-31.
[11]	PALANISAMY B, LIU Ling, LEE K, et al.Anonymizing Continuous Queries with Delay-tolerant Mix-zones over Road Networks[J]. Distributed and Parallel Databases, 2014, 32(1): 91-118.
[12]	HWANG R H, HSUEH Y L, CHUNG H W.A Novel Time-obfuscated Algorithm for Trajectory Privacy Protection[J]. IEEE Transactions on Services Computing, 2014, 7(2): 126-139.
[13]	ZEBERGA K, JIN R, CHO H J, et al. A Safe-region Approach to a Moving k-RNN Queries in a Directed Road Network[EB/OL]. , 2016-11-25.
[14]	肖剑川,许力,叶阿勇,等. 基于Voronoi图的路网轨迹隐私保护研究[J]. 信息网络安全,2016(6):15-21.
[15]	张磊,马春光,杨松涛,等. 基于轮廓泛化的位置隐私保护模型及方法[J]. 系统工程与电子技术,2016,38(12):2894-2900.
[16]	王勇,高诗梦,聂肖,等. 基于速度动态差异的位置服务匿名算法[J]. 信息网络安全,2015(4):28-35.
[17]	张磊,马春光,杨松涛,等. 关联概率不可区分的位置隐私保护方法[J]. 通信学报,2017,38(8):37-49.
[18]	张磊,马春光,杨松涛,等. 基于属性基加密的用户协作连续查询隐私保护策略[J]. 通信学报,2017,38(9):76-85.
[19]	ZHANG Lei, LI Jing, YANG Songtao, et al.Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query[J]. Wireless Personal Communications, 2017, 96(2): 2305-2322.
[20]	周凯,彭长根,何建琼,等. 可证明安全的LBS中连续查询的轨迹隐私保护方案[J]. 信息网络安全,2017(1):43-47.
[21]	张磊,马春光,杨松涛. 基于位置关联相似性的匿名算法[J]. 中国科技论文,2016,11(2):197-201.
[22]	PALANISAMY B, LIU Ling.Attack-Resilient Mix-zones over Road Networks: Architecture and Algorithms[J]. IEEE Transactions on Mobile Computing, 2015, 14(3): 495-508.

基于Hash函数的属性泛化隐私保护方案

A Hash Function-based Attribute Generalization Privacy Protection Scheme

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	唐春明, 林旭慧. 隐私保护集合交集计算协议[J]. 信息网络安全, 2020, 20(1): 9-15.
[2]	汪金苗, 王国威, 王梅, 朱瑞瑾. 面向雾计算的隐私保护与访问控制方法[J]. 信息网络安全, 2019, 19(9): 41-45.
[3]	郝文江, 林云. 互联网企业社会责任现状与启示研究[J]. 信息网络安全, 2019, 19(9): 130-133.
[4]	周权, 许舒美, 杨宁滨. 一种基于ABGS的智能电网隐私保护方案[J]. 信息网络安全, 2019, 19(7): 25-30.
[5]	李怡霖, 闫峥, 谢皓萌. 车载自组织网络的隐私保护综述[J]. 信息网络安全, 2019, 19(4): 63-72.
[6]	傅彦铭, 李振铎. 基于拉普拉斯机制的差分隐私保护k-means++聚类算法研究[J]. 信息网络安全, 2019, 19(2): 43-52.
[7]	赵志岩, 吴剑, 康凯. 一种兼顾业务数据安全的隐私保护世系发布方法[J]. 信息网络安全, 2019, 19(12): 29-37.
[8]	胡荣磊, 何艳琼, 曾萍, 范晓红. 一种大数据环境下医疗隐私保护方案设计与实现[J]. 信息网络安全, 2018, 18(9): 48-54.
[9]	李佩丽, 徐海霞, 马添军, 穆永恒. 区块链技术在网络互助中的应用及用户隐私保护[J]. 信息网络安全, 2018, 18(9): 60-65.
[10]	马蓉, 陈秀华, 刘慧, 熊金波. 移动群智感知中用户隐私度量与隐私保护研究[J]. 信息网络安全, 2018, 18(8): 64-72.
[11]	赖成喆, 王文娟. 面向车队的安全且具备隐私保护的移动性管理框架[J]. 信息网络安全, 2018, 18(7): 36-46.
[12]	王乐, 杨哲荣, 刘容京, 王祥. 基于属性加密算法的可穿戴设备系统隐私保护方法研究[J]. 信息网络安全, 2018, 18(6): 77-84.
[13]	张磊, 王斌, 于莉莉. 基于马尔可夫预测的连续查询隐私保护方法[J]. 信息网络安全, 2018, 0(5): 12-12.
[14]	李梁, 张应辉, 邓恺鑫, 张甜甜. 5G智能电网中具有隐私保护的电力注入系统[J]. 信息网络安全, 2018, 18(12): 87-92.
[15]	赵娜, 龙慧, 苏金树. 一种适用于物联网环境的匿名认证与隐私保护方案[J]. 信息网络安全, 2018, 18(11): 1-7.