[1]陈春玲,熊晶,陈琳,等. 加权社会网络中的个性化隐私保护算法[J].计算机技术与发展,2016,26(08):88-92.
　CHEN Chun-ling,XIONG Jing,CHEN Lin,et al. Personalized Privacy Preservation Algorithm in Weighted Social Networks[J].,2016,26(08):88-92.
点击复制

 加权社会网络中的个性化隐私保护算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年08期
页码:
88-92
栏目:
安全与防范
出版日期:
2016-08-10

文章信息/Info

Title:
 Personalized Privacy Preservation Algorithm in Weighted Social Networks
文章编号:
1673-629X（2016)08-0088-05
作者:
 陈春玲; 熊晶; 陈琳; 余瀚
 南京邮电大学 计算机学院 软件学院
Author(s):
 CHEN Chun-ling ; XIONG Jing; CHEN Lin; YU Han
关键词:
 ; 加权社会网络; 隐私保护; 个性化; 权重包; 敏感属性
Keywords:
 ; weighted social network; privacy preservation; personalization; weight bag; sensitive attributes
分类号:
TP301.6
文献标志码:
A
摘要:
 针对加权社会网络中存在一部分用户不需要隐私保护或者需要某种特殊隐私保护的现象，提出了一种基于加权社会网络数据发布的个性化隐私保护方法。将社会网络中的隐私保护分为3个等级：不需保护L=0、防止权重包攻击L=1和防止敏感属性泄漏L =2。对于L≠0的节点集，通过k-度分组和修改权重包信息对节点进行匿名，使得每个分组满足权重包k-匿名；在分组过程中，对于存在L =2的分组要求其敏感属性满足l-diversity。理论分析和实验表明：攻击者不能以大于1/ k的概率识别出某节点，且不能以大于1/ l的概率推断出节点的敏感信息。该方法能够满足社会网络中各用户对隐私保护的要求，同时降低了社会网络图的信息损失。
Abstract:
 There is a phenomenon that some users do not need privacy protections or they need special privacy protections in social net-works,so a personalized privacy protection method to meet these requirements is proposed based on weighted social network. The privacy protection is divided into three levels:without protection ( L =0),preventing the weight bags attack ( L =1),and preventing the sensi-tive attributes disclosure ( L =2). For nodes with L≠0, k-degree grouping and weight bag modifying is used to the anonymous nodes, which makes each group meets the k anonymity of weight bag. In the process of grouping,the group with L =2 has to ensure l-diversity for sensitive attributes. Theoretical analysis and experiments show that attackers can’ t identify a node with the probability over 1/ k and infer node’ s sensitive attribute with the probability over 1/ l . The method satisfies the user’ s requirements in weighted social network, and the information loss is reduced.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(08):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(08):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(08):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(08):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(08):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(08):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(08):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(08):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(08):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(08):47.

更新日期/Last Update: 2016-09-29