基于信息损失量估计的匿名图构造方法

苏洁; 刘帅; 罗智勇; 孙广路

doi:10.11959/j.issn.1000-436x.2016116

您当前的位置：

首页 >

文章列表页 >

基于信息损失量估计的匿名图构造方法

学术论文 | 更新时间：2024-06-05

- 基于信息损失量估计的匿名图构造方法
- Method of constructing an anonymous graph based on information loss estimation
- 通信学报 2016年37卷第6期页码：56-64
- 作者机构：
  
  哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
- 作者简介：
  
  [ "苏洁（1979-），女，山东淄博人，哈尔滨理工大学副教授、硕士生导师，主要研究方向为智能信息处理。" ]
  [ "刘帅（1988-），男，山东济宁人，哈尔滨理工大学硕士生，主要研究方向为智能信息处理。" ]
  [ "罗智勇（1978-），男，黑龙江大庆人，哈尔滨理工大学副教授、硕士生导师，主要研究方向为智能信息处理。" ]
  [ "孙广路（1979-），男，黑龙江哈尔滨人，哈尔滨理工大学教授、硕士生导师，主要研究方向为计算机网络与信息安全、机器学习。" ]
- 基金信息：
  
  黑龙江省自然科学基金资助项目(A201301);黑龙江省教育科学规划课题基金资助项目(GBC1211062);黑龙江省普通高等学校新世纪优秀人才培养计划基金资助项目(1155-ncet-008);黑龙江省博士后基金资助项目(LBH-Z12082)
- DOI：10.11959/j.issn.1000-436x.2016116
  中图分类号： TP309.2
- 网络首发：2016-06，
  
  纸质出版：2016-06-25
- 稿件说明：
移动端阅览
苏洁, 刘帅, 罗智勇, 等. 基于信息损失量估计的匿名图构造方法[J]. 通信学报, 2016,37(6):56-64.

Jie SU, Shuai LIU, Zhi-yong LUO, et al. Method of constructing an anonymous graph based on information loss estimation[J]. Journal on Communications, 2016, 37(6): 56-64.
苏洁, 刘帅, 罗智勇, 等. 基于信息损失量估计的匿名图构造方法[J]. 通信学报, 2016,37(6):56-64. DOI： 10.11959/j.issn.1000-436x.2016116.

Jie SU, Shuai LIU, Zhi-yong LUO, et al. Method of constructing an anonymous graph based on information loss estimation[J]. Journal on Communications, 2016, 37(6): 56-64. DOI： 10.11959/j.issn.1000-436x.2016116.

摘要

首先分析了在进化的社会网络序列中，攻击者利用节点度信息，通过识别目标节点的方法对局部社会网络进行攻击过程，分析了利用k匿名方法对该类攻击进行隐私保护时存在的信息损失问题，针对该问题，提出了一种基于信息损失量估计的k匿名图流构造方法，通过子图节点属性泛化、子图内部结构的泛化控制图重构的信息损失，通过禁止子图内部扰动阻止网络攻击。定义匿名过程中由于图重构造成的节点和结构信息损失的估算方法，建立了基于贪婪聚类算法的网络节点的k匿名聚类算法，根据信息损失估计实现匿名分组，在进化的社会网络中以最小信息损失量构造匿名社会网络，在医疗诊断数据集上的实验表明所提方法能够较理想地控制信息损失量。

Abstract

A potential attack based on degree information by re-identifying target vertexes from a sequence of published graphs was analyzed.To deal with this kind of attack

a k-anonymous graph stream constructing method based on information loss estimation was provided.Information loss caused by re-constructing graph was controlled by using the method of attributes generalization of nodes and the structure generalization of sub-graph.The disturbance in sub-graph was forbidden to prevent the attack.The method of measuring the information loss of nodes and structures during the anonymous process due to re-construction of graph was defined.A k-anonymity cluster algorithm based on greedy clustering algorithm was build

which realized anonymous partition according to the information loss.Finally

a method of constructing anonymous social network for the evolving social network with the least information loss was provided.The experiments on medical diagnostic data set show that the algorithm of constructing anonymous graph based on the information loss estimation can be used to control the loss of information.

关键词

Keywords

references

韩毅 , 方滨兴 , 贾焰 , 等 . 基于密度估计的社会网络特征簇挖掘方法 [J ] . 通信学报 , 2012 , 33 ( 5 ): 38 - 48 .

HAN Y , FANG B X , JIA Y , et al . Mining characteristic clusters:a density estimation approach [J ] . Journal on Communications , 2012 , 33 ( 5 ): 38 - 48 .

WU X , YING X , LIU K . A survey of privacy-preservation of graphs and social networks [M ] . Managing and mining graph data . Springer US , 2010 : 421 - 453 .

CASAS-ROMA J , HERRERA-JOANCOMARTÍ J , TORRA V . Anonymizing graphs:measuring quality for clustering [J ] . Knowledge＆Information Systems , 2015 , 44 ( 3 ): 1 - 22 .

BHAGAT S , CORMODE G , KRISHNAMURTHY B . Class based graph anaonymization for social network data [C ] // 35th International Conference on Very Large Data Base . 2009 : 766 - 777 .

WANG R , ZHANG M , FENG D , et al . A clustering approach for privacy-preserving in social networks [C ] // Information Security and Cryptology-ICISC 2014 . Springer International Publishing , 2014 : 193 - 204 .

JING Y , GOSSWEILER III R C . Using visualization techniques for adjustment of privacy settings in social networks [P ] . US8832567 , 2014 .

AGGARWAL C C , LI Y , YU P S . On the anonymizability of graphs [J ] . Knowledge＆Information Systems , 2015 , 45 ( 3 ): 571 - 588 .

兰丽辉 , 鞠时光 . 基于差分隐私的权重社会网络隐私保护 [J ] . 通信学报 , 2015 , 36 ( 9 ): 145 - 159 .

LAN L H , JU S G . Privacy preserving based on differential privacy for weighted social networks [J ] . Journal on Communications , 2015 , 36 ( 9 ): 145 - 159 .

KARWA V , SLAVKOVIC A B , KRIVITSKY P N . Differentially private exponential random graphs [C ] // Privacy in Statistical Database-UNESCO Chair in Data Privacy,International Conference,PSD 2014 . Ibiza,Spain , 2014 : 143 - 155 .

SALA A , ZHAO X , WILSON C . Sharing graphs using differentially private graph models [C ] // The 2011 ACM SIGCOMM Conference on Internet Measurement,ACM , 2011 : 81 - 98 .

MEDFORTH N , WANG K . Privacy risk in graph stream publishing for social network data [C ] // The 2011 IEEE 11th International Conference on Data Mining . 2011 : 437 - 446 .

ROSSI L , MUSOLESI M , TORSELLO A . On the k-anonymization of time-varying and multi-layer social graphs [J ] . arXiv preprint arXiv:1503.06497 , 2015 .

ZHOU B , PEI J . The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks [J ] . Knowledge and Information Systems , 2011 , 28 ( 1 ): 47 - 77 .

LIU C G , LIU I H , YAO W S , et al . K-anonymity against neighborhood attacks in weighted social networks [J ] . Security ＆Communication Networks , 2015 , 18 ( 8 ): 3864 - 3882 .

LIU K , TERZI E . Towards identity anonymization on graphs [C ] // The 2008 ACM SIGMOD International Conference on Management of Data . ACM , 2008 : 93 - 106 .

CHENG J , FU A W , LIU J . K-isomorphism:privacy preserving network publication against structural attacks [C ] // The 2010 ACM SIGMOD International Conference on Management of Data . ACM , 2010 : 459 - 470 .

MICHEAL H , GEROME M , DAVID J . Resisting structural re-identification in anonymized social networks [J ] . Proceedings of the VLDB Endowment , 2008 , 1 ( 1 ): 102 - 114 .

FUNG B C M , JIN Y , LI J , et al . Anonymizing social network data for maximal frequent-sharing pattern mining [M ] . Recommendation and Search in Social Networks . Springer International Publishing , 2015 : 77 - 100 .

SWEENEY L . k-anonymity:a model for protecting privacy [J ] . International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems , 2002 , 10 ( 05 ): 557 - 570 .

BYUN J W , KAMRA A , BERTINO E , et al . Efficient k-anonymization using clustering techniques [C ] // Dasfaa . Springer Berlin Heidelberg , 2007 : 188 - 200 .

HAN J , KAMBER M . Data mining:comcepts and techniques [J ] . San Francisco , 2006 , 29 ( 1 ): 1 - 25 .

浏览量

1287

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于SM9的周期性可否认环签密方案的设计

基于SM4算法的隐私保护轻量化人脸认证方案

支持类型验证的多维数据选择性聚合方案

基于群签名的慈善捐赠身份隐私安全保护方案

面向众包的隐私保护任务匹配方案