位置大数据服务中基于差分隐私的数据发布技术

doi:10.11959/j.issn.1000-436x.2016176

通信学报 ›› 2016, Vol. 37 ›› Issue (9): 46-54.doi: 10.11959/j.issn.1000-436x.2016176

位置大数据服务中基于差分隐私的数据发布技术

张琳^1,^2,³,刘彦¹,王汝传^1,^2,³

¹ 南京邮电大学计算机学院，江苏南京 210003
² 江苏省无线传感网高技术研究重点实验室，江苏南京 210003
³ 南京邮电大学宽带无线通信与传感网技术教育部重点实验室，江苏南京 210003

出版日期:2016-09-25 发布日期:2016-09-28
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;江苏省科技支撑计划基金资助项目;江苏省属高校自然科学研究重大基金资助项目;江苏省属高校自然科学研究重大基金资助项目

Location publishing technology based on differential privacy-preserving for big data services

Lin ZHANG^1,^2,³,Yan LIU¹,Ru-chuan WANG^1,^2,³

¹ College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
² Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing 210003,China
³ Key Lab of Ministry of Education Broadband Wireless Communication and Sensor Network Technology of Nanjing University of Posts and Telecommunications,Nanjing 210003,China

Online:2016-09-25 Published:2016-09-28
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;Scientific and Technological Support Project of Jiangsu Province;Natural Science Key Fund for Col-leges and Universities of Jiangsu Province;Natural Science Key Fund for Col-leges and Universities of Jiangsu Province

摘要/Abstract

摘要：

应对多组合复杂攻击及前景知识攻击，提出一种新的基于差分隐私保护机制的位置大数据发布模型，创新设计可用性评估反馈机制模块，引入时间变量动态地针对敏感属性以及身份识别等分析模型的服务质量，能在位置大数据与非位置大数据相结合、用户背景知识不确定等情况下保护用户的位置隐私。仿真实验基于多种空间索引技术验证了新发布模型能够在指定的私密性条件下，为位置查询服务发布具有更高准确率的匿名数据。

关键词: 位置服务, 大数据, 差分隐私, 数据发布, 隐私保护

Abstract:

Aiming at dealing with prospect knowledge and complex combinatorial attack,a new location big data publishing mechanism under differential privacy technology was given.And innovative usability evaluation feedback mechanism was designed.It gave corresponding solution details for the sensitive attributes and the identity recognition to analyze the quality of service,aimed at privacy protecting for location based big data under situations like combination of location information and non-location information and attacker’s arbitrary background knowledge.Simulation results based on different spatial indexing technology proved that the new publishing model has a higher accuracy under specified privacy conditions for the location query service.

Key words: location service, big data, differential privacy, data publishing, privacy-preserving

张琳,刘彦,王汝传. 位置大数据服务中基于差分隐私的数据发布技术[J]. 通信学报, 2016, 37(9): 46-54.

Lin ZHANG,Yan LIU,Ru-chuan WANG. Location publishing technology based on differential privacy-preserving for big data services[J]. Journal on Communications, 2016, 37(9): 46-54.

图/表 9

图1

图2

图3

表1

图4

图5

图6

图7

图8

参考文献 19

[1]	王璐, 孟小峰 . 位置大数据隐私保护研究综述[J]. 软件学报, 2014,25(4): 693-712. WANG L，MENG X F . Location privacy preservation in big data era:a survey[J]. Journal of Software, 2014,25(4): 693-712.
[2]	霍峥, 孟小峰 . 轨迹隐私保护技术研究[J]. 计算机学报, 2011,34(10): 1820-1830. HUO Z , MENG X F . A survey of trajectory privacy-preserving techniques[J]. Chinese Journal of Computers, 2011,34(10): 1820-1830.
[3]	KIDO H , YANAGISAWA Y , SATOH T . Protection of location privacy using dummies for location-based services[C]// The 21st International Conference on Data Engineering Workshops,ICDEW IEEE Computer Society. Washington,DC, 2005: 1248-1254.
[4]	MOKBEL M F , CHOW C Y , AREF W G . The new casper:query processing for location services without compromising privacy[C]// The 32nd International Conference on Very Large Data Bases,VLDB Endowment. 2006: 763-774.
[5]	CHOW C Y , MOKBEL M F . Trajectory privacy in location-based services and data publication[J]. ACM SIGKDD Explorations Newsletter, 2011,13(1): 19-29.
[6]	BAMBA B , LIU L , PESTI P ,et al. Supporting anonymous location queries in mobile environments with privacygrid[C]// WWW,ACM, 2008: 237-246.
[7]	DWORK C , . Differential privacy[C]// The 33rd International Colloquium on Automata,Languages and Programming (ICALP). Venice,Italy, 2006: 1-12.
[8]	DWORK C , . Differential privacy:a survey of results[C]// The 5th International Conference on Theory and Applications of Models of Computation (TAMC). Xi’an,China, 2008: 1-19.
[9]	张啸剑, 孟小峰 . 面向数据发布和分析的差分隐私保护[J]. 计算机学报, 2014,37(4): 927-949. ZHANG X J , MENG X F . Differential privacy in data publication and analysis[J]. Chinese Journal of Computers, 2014,37(4): 927-949.
[10]	丁丽萍, 卢国庆 . 面向频繁模式挖掘的差分隐私保护研究综述[J]. 通信学报, 2014,35(10): 200-209. DING L P , LU G Q . Survey of differential privacy in frequent pattern mining[J]. Journal on Communications, 2014,35(10): 200-209.
[11]	CHEN R , ACS G , CASTELLUCCIA C . Differentially private sequential data publication via variable-length n-grams[C]// The 2012 ACM Conferenceon Computer and Communications Security,CCS,ACM,NewYork, 2012: 638-649.
[12]	DEWRI R . Local differential perturbations:location privacy under approximate knowledge attackers[J]. IEEE Trans on Mobile Computing, 2013,12(12): 2360-2372.
[13]	叶阿勇, 李亚成, 马建峰 ,等. 基于服务相似性的 k-匿名位置隐私保护方法[J]. 通信学报, 2014,35(11): 162-169. YE A Y , LI Y C , MA J F ,et al. Location privacy-preserving method of k-anonymous based on service similarity[J]. Journal on Communications, 2014,35(11): 162-169.
[14]	HO S S , RUAN S . Differential privacy for location pattern mining[C]// SPRINGL,ACM. 2011: 17-24.
[15]	ZHANG J D , GHINITA G , CHOW C Y . Differentially private location recommendations in geosocial networks[C]// IEEE 15th International Conference on MDM, 2014: 59-68.
[16]	DWORK C , MCSHERRY F , NISSIM K ,et al. Calibrating noise to sensitivity in private data analysis[C]// The 3th Theory of Cryptography Conference (TCC). New York,USA, 2006: 363-385.
[17]	MCSHERRY F , TALWAR K . Mechanism design via differential privacy[C]// The 48th Annual IEEE symposium on Foundations of Computer Science (FOCS). Providence,RI,USA, 2007: 94-103.
[18]	CORMODE G , PROCOPIUC C , SRIVASTAVA D . Differentially private spatial decompositions[C]// IEEE 28th International Conference on Data Engineering (ICDE). 2012: 20-31.
[19]	杨晓春, 王雅哲, 王斌 ,等. 数据发布中面向多敏感属性的隐私保护方法[J]. 计算机学报, 2008,31(4): 574-587. YANG X C , WANG Y Z , WANG B ,et al. Privacy preserving approaches for multiple sensitive attributes in data publishing[J]. Chinese Journal of Computers, 2008,31(4): 574-587.

数据集		属性集
数据集	位置数据	非位置数据
	纬度	交通方式
GeoLife (38 494)	经度	生活方式
	国家、城市、街道	社交信息搜索行为
Diversification	纬度	共享信息
Dataset Div400	经度	文字图片
(43 418)	国家、城市、街道	个人数据文件夹
		用户账号
		用户交易信息
Amazon Access
	用户定位信息	用户职业
Samples (60 000)
		用户工作单位
		家庭基本信息

位置大数据服务中基于差分隐私的数据发布技术

Location publishing technology based on differential privacy-preserving for big data services

在线阅读

PDF下载

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 19

相关文章 15

Metrics

推荐阅读 0

[1]	马鑫迪, 李清华, 姜奇, 马卓, 高胜, 田有亮, 马建峰. 面向Non-IID数据的拜占庭鲁棒联邦学习[J]. 通信学报, 2023, 44(6): 138-153.
[2]	冯涛, 陈李秋, 方君丽, 石建明. 基于本地化差分隐私和属性基可搜索加密的区块链数据共享方案[J]. 通信学报, 2023, 44(5): 224-233.
[3]	余晟兴, 陈泽凯, 陈钟, 刘西蒙. DAGUARD：联邦学习下的分布式后门攻击防御方案[J]. 通信学报, 2023, 44(5): 110-122.
[4]	夏莹杰, 朱思雨, 刘雪娇. 区块链架构下具有条件隐私的车辆编队跨信任域高效群组认证研究[J]. 通信学报, 2023, 44(4): 111-123.
[5]	胡柏吉, 张晓娟, 李元诚, 赖荣鑫. 支持多功能的V2G网络隐私保护数据聚合方案[J]. 通信学报, 2023, 44(4): 187-200.
[6]	徐明, 张保俊, 伍益明, 应晨铎, 郑宁. 面向网络攻击和隐私保护的多智能体系统分布式共识算法[J]. 通信学报, 2023, 44(3): 117-127.
[7]	张淑芬, 董燕灵, 徐精诚, 王豪石. 基于目标扰动的AdaBoost算法[J]. 通信学报, 2023, 44(2): 198-209.
[8]	余晟兴, 陈钟. 基于同态加密的高效安全联邦学习聚合框架[J]. 通信学报, 2023, 44(1): 14-28.
[9]	汤凌韬, 王迪, 刘盛云. 面向非独立同分布数据的联邦学习数据增强方案[J]. 通信学报, 2023, 44(1): 164-176.
[10]	金伟, 李凤华, 余铭洁, 郭云川, 周紫妍, 房梁. 面向HDFS的密钥资源控制机制[J]. 通信学报, 2022, 43(9): 27-41.
[11]	袁程胜, 郭强, 付章杰. 基于差分隐私的深度伪造指纹检测模型版权保护算法[J]. 通信学报, 2022, 43(9): 181-193.
[12]	王瀚仪, 李效光, 毕文卿, 陈亚虹, 李凤华, 牛犇. 多级本地化差分隐私算法推荐框架[J]. 通信学报, 2022, 43(8): 52-64.
[13]	张学旺, 黎志鸿, 林金朝. 基于公平盲签名和分级加密的联盟链隐私保护方案[J]. 通信学报, 2022, 43(8): 131-141.
[14]	张勇, 李丹丹, 韩璐, 黄小红. 隐私保护的群体感知数据交易算法[J]. 通信学报, 2022, 43(5): 1-13.
[15]	王继锋, 王国峰. 边缘计算模式下密文搜索与共享技术研究[J]. 通信学报, 2022, 43(4): 227-238.