大规模社会网络K-出入度匿名方法

doi:10.19678/j.issn.1000-3428.0056038

计算机工程 ›› 2020, Vol. 46 ›› Issue (11): 164-173. doi: 10.19678/j.issn.1000-3428.0056038

大规模社会网络K-出入度匿名方法

张晓琳¹, 刘娇¹, 毕红净², 李健¹, 王永平¹

1. 内蒙古科技大学信息工程学院, 内蒙古包头 014010;
2. 唐山师范学院计算机科学系, 河北唐山 063000

收稿日期:2019-09-18 修回日期:2019-11-06 发布日期:2019-11-15
作者简介:张晓琳(1966-),女,教授,主研方向为大规模社会网络隐私保护;刘娇,硕士研究生;毕红净,讲师、硕士;李健,硕士研究生;王永平,讲师。
基金资助:
国家自然科学基金"面向云计算环境的大规模社会网络隐私保护技术研究"（61562065）；内蒙古自治区自然科学基金"有效保护社区结构的大规模社会网络隐私保护技术研究"（2019MS06001）。

K-In&Out-Degree Anonymity Method for Large Scale Social Networks

ZHANG Xiaolin¹, LIU Jiao¹, BI Hongjing², LI Jian¹, WANG Yongping¹

1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China;
2. Department of Computer Science, Tangshan Normal University, Tangshan, Hebei 063000, China

Received:2019-09-18 Revised:2019-11-06 Published:2019-11-15

摘要/Abstract

摘要： 现有社会网络隐私保护技术在处理大规模社会网络有向图时数据处理效率较低，且匿名数据发布通常不能满足社区结构分析的需求。为此，提出一种基于层次社区结构的大规模社会网络K-出入度匿名（KIODA）算法。该算法基于层次社区结构划分社区，采用贪心算法分组并匿名K-出入度序列，分布式并行添加虚拟节点以实现K-出入度匿名，基于GraphX图数据处理平台传递节点间的信息，根据层次社区熵的变化情况选择虚拟节点对并进行合并删除，从而减少信息损失。实验结果表明，KIODA算法在处理大规模社会网络有向图数据时具有较高的执行效率，并在匿名后保证了数据发布时社区结构分析结果的可用性。

关键词: 层次社区结构, 社会网络有向图, K-出入度匿名, 社区划分, GraphX框架

Abstract: Existing privacy protection techniques are inefficient when applied to directed graphs of large-scale social networks,and publishing anonymous data does not meet the needs of community structure analysis.To address the problem,this paper proposes a K-In&Out-Degree Anonymity(KIODA) algorithm for large-scale social networks based on hierarchical community structure.The algorithm divides the community based on hierarchical community structure.The greedy algorithm is used to group K-in&out-degree sequences and make them anonymous,and the virtual nodes are added in parallel to achieve K-in&out-degree anonymity.Then information exchanges between nodes are implemented based on the GraphX platform.Virtual node pairs are selected based on the changes of the hierarchical community entropy,and are merged and deleted to reduce information loss.Experimental results show that the KIODA algorithm improves the efficiency of processing directed graphs of large-scale social networks,and ensures the availability of community structure analysis results in data publishing after the anonymity is realized.

Key words: hierarchical community structure, directed graph of social networks, K-In&Out-Degree Anonymity(KIODA), community division, GraphX framework

中图分类号:

TP391

张晓琳, 刘娇, 毕红净, 李健, 王永平. 大规模社会网络K-出入度匿名方法[J]. 计算机工程, 2020, 46(11): 164-173.

ZHANG Xiaolin, LIU Jiao, BI Hongjing, LI Jian, WANG Yongping. K-In&Out-Degree Anonymity Method for Large Scale Social Networks[J]. Computer Engineering, 2020, 46(11): 164-173.

https://www.ecice06.com/CN/Y2020/V46/I11/164

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