中心性
伪装
互联网隐私
邻里(数学)
启发式
社交网络(社会语言学)
社会网络分析
互联网
网络分析
简单(哲学)
计算机安全
计算机科学
社会学
数据科学
社会心理学
社会化媒体
心理学
万维网
人工智能
工程类
哲学
数学分析
电气工程
组合数学
认识论
数学
作者
Marcin Waniek,Tomasz Michalak,Michael Wooldridge,Talal Rahwan
标识
DOI:10.1038/s41562-017-0290-3
摘要
The Internet and social media have fueled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools? By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence, and security agencies may better understand how terrorists escape detection. We first study how an individual can evade "network centrality" analysis without compromising his or her influence within the network. We prove that an optimal solution to this problem is hard to compute. Despite this hardness, we demonstrate that even a simple heuristic, whereby attention is restricted to the individual's immediate neighbourhood, can be surprisingly effective in practice. For instance, it could disguise Mohamed Atta's leading position within the WTC terrorist network, and that is by rewiring a strikingly-small number of connections. Next, we study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment, expressing how well a community is hidden, and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either "unfriend" certain other members, or "befriend" some non-members, in a coordinated effort to camouflage their community.
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