已发布社交网络中抵制社区认同的结构多样性
Structural Diversity for Resisting Community Identification in Published Social Networks
摘要：
随着越来越多的社交网络数据被发布和共享用于商业和研究目的，关于社交网络中的个人的隐私问题已经成为严重的问题。顶点识别是基于顶点度等背景知识从网络中识别特定用户的一个重要问题。然而，在现实中，社交网络中的每个个体倾向于不仅与顶点身份相关联，而且与社区身份相关联，社区身份可以表示对公众敏感的个人隐私信息，例如政党归属。本文首先解决了新的隐私问题，被称为社区识别，通过显示，即使社会网络的保护，现有的匿名计划的受害者的社区身份仍然可以推断。针对这个问题，我们提出了\textit{structural diversity}的概念来提供社区身份的匿名性。$k$ -结构多样性分析（$k$ -SDA）是为了确保在社交网络中的至少$k$社区中具有相同顶点度的足够顶点。我们提出了一个可扩展的编程公式，以找到最佳的解决方案，$k$ -SDA，也设计了可扩展的算法，以解决大规模的情况下，$k$ -SDA从不同的角度。真实数据集上的性能研究从不同的角度证明了所提出的隐私方案和我们的匿名化方法的实际效用。
英文摘要：
As an increasing number of social networking data is published and shared for commercial and research purposes, privacy issues about the individuals in social networks have become serious concerns. Vertex identification, which identifies a particular user from a network based on background knowledge such as vertex degree, is one of the most important problems that has been addressed. In reality, however, each individual in a social network is inclined to be associated with not only a vertex identity but also a community identity, which can represent the personal privacy information sensitive to the public, such as political party affiliation. This paper first addresses the new privacy issue, referred to as community identification, by showing that the community identity of a victim can still be inferred even though the social network is protected by existing anonymity schemes. For this problem, we then propose the concept of \textit{structural diversity} to provide the anonymity of the community identities. The $k$-Structural Diversity Anonymization ($k$-SDA) is to ensure sufficient vertices with the same vertex degree in at least $k$ communities in a social network. We propose an Integer Programming formulation to find optimal solutions to $k$-SDA and also devise scalable heuristics to solve large-scale instances of $k$-SDA from different perspectives. The performance studies on real data sets from various perspectives demonstrate the practical utility of the proposed privacy scheme and our anonymization approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
