远程连接对复杂网络中节点匿名性的影响
Rachel G de Jong1,2, Mark P J van der Loo3,4, Frank W Takes3
1Leiden University, LIACS, 2333 CA, Leiden, The Netherlands. r.g.de.jong@liacs.leidenuniv.nl.
Scientific reports
|January 11, 2024
概括
新的方法,d-k-匿名和匿名级联,改善了社交网络分析中的隐私评估. 即使是远程连接也会显著降低匿名性,影响数据共享策略.
科学领域:
- 计算机科学 计算机科学
- 社交网络分析 社交网络分析
- 网络安全 网络安全
背景情况:
- 个人隐私在社交网络分析研究中至关重要.
- 基于自我网络的现有匿名措施是不够的.
- 远程连接带来了显著的匿名化风险.
研究的目的:
- 开发用于评估网络匿名性的新型指标.
- 为了考虑远程连接对隐私的影响.
- 为大规模网络分析提供灵活的解决方案.
主要方法:
- 引入d-k-匿名性,考虑到距离d的信息.
- 开发匿名级联来捕捉独特的"传染性".
- 整合了对图形结构的"双节点"处理.
主要成果:
- 实验表明,当考虑2距离的连接时,匿名性会显著减少.
- 将分析扩展到自我网络之外,通过一个额外的链接,匿名性降低了50%以上.
- 拟议的方法可针对精度和计算时间进行调整,从而实现大规模的网络分析.
结论:
- 传统的以自我网络为基础的匿名措施是不够的.
- 远程连接在网络中极大地影响了个人的隐私.
- 这些发现需要修改隐私意识共享敏感网络数据的策略.
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