社交网络中的社团隐私研究综述

doi:10.11959/j.issn.2096-109x.2021021

摘要/Abstract

摘要：

社团是社交网络的重要特征，社团检测技术的发展给网络用户带来隐私泄露的危险。如何保护敏感的社团信息不被泄露，保障用户与社团安全已经成为网络安全领域的研究热点。近几年，社团保护技术取得了初步进展，但针对社交网络中的社团隐私或社团安全研究进展综述较少，不利于该研究方向的长远发展。因此，主要针对社团结构隐私方面的研究进展进行综述，对最新的相关研究成果进行系统地归纳、总结、比较，并展望了未来的研究方向。

关键词: 社交网络, 社团检测, 社团隐私, 社团安全, 社团隐藏

Abstract:

Community is an important feature of social network and the development of community detection technology brings the danger of privacy disclosure to network users.How to protect sensitive community information from being leaked and ensure the security of users and communities has become a research hotspot in the field of network security.In recent years, community privacy protection technology has made initial progress, but there is a few survey on the research of community privacy or community security in social networks, and it may limit the potential and long-term development of this research topic.The research on the privacy of community structure was mainly reviewed and the related works on community security were classified, summarized, compared.The hot issues of community security in the future were proposed.

Key words: social network, community detection, community privacy, community safety, community hiding

中图分类号:

TP309

蒋忠元, 陈贤宇, 马建峰. 社交网络中的社团隐私研究综述[J]. 网络与信息安全学报, 2021, 7(2): 10-21.

Zhongyuan JIANG, Xianyu CHEN, Jianfeng MA. Survey of community privacy in social network[J]. Chinese Journal of Network and Information Security, 2021, 7(2): 10-21.

图/表 7

表1

图1

表2

图2

图3

图4

表3

基于全社团中隐私保护的相关工作对比Table 3 Comparisons of related work based on privacy protection on the all communities"

文献	操作	技术	不足之处
文献[27]	增边或删边	K-同构、 $K_{-}^{+}$ 同构	节点角色不足、算法优化
文献[28]	匿名化	分级隐私保护	等级划分依据单一、薄弱

表3

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社交网络	节点数目	边数目	描述
kar	34	78	Zachary Karate's Club
dol	62	159	Dolphins association
mad	62	159	Madrid Train Bombing
lesm	77	154	Co-appearances of characters in Les Miserables
polb	105	441	Books about US politics
words	112	425	Adjectives/nouns in the novel David Copperfield
nets	1 589	5 486	Net Theory co-authors
erdos	6 100	9 939	Erdos collaborations
faceb	6 387	2.17×10⁵	Facebook
pow	4 941	6 594	USA Power Grid
astr	1.79×10⁴	1.97×10⁵	Astrophysics co-authors
4sq	6.39×10⁵	3.2×10⁶	Foursquare
dblp	5.405×10⁵	1.52×10⁷	DBLP
amz	5.48×10⁵	9.25×10⁵	Amazon metadata
you	1×10⁶	3×10⁶	Youtube dataset
ork	3×10⁶	1.172×10⁸	Orkut social network

文献	操作	技术	所需网络知识	不足之处
文献[22]	增边、删边	安全性、模块度	目标社团节点的边	检测单一、反欺骗问题
文献[23]	增边、删边	新的安全性	目标社团节点的边	检测单一、真实应用
文献[24]	增边、删边	模块度	目标社团节点的边	研究知识不足、有局限性
文献[25]	增边	模块度	节点中心性	网络规模小、关注点单一
文献[26]	增边、删边	模块度、遗传算法	目标社团节点的边	检测单一、单目标优化