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首页> 外文期刊>IEEE transactions on dependable and secure computing >Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model
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Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model

机译：基于知识图模型的社交网络去匿名化和隐私推断

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摘要

Social network data is widely shared, transferred and published for research purposes and business interests, but it has raised much concern on users' privacy. Even though users' identity information is always removed, attackers can still de-anonymize users with the help of auxiliary information. To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. However, most existing approaches assume specific and restrict network structure as background knowledge and ignore semantic level prior belief of attackers, which are not always realistic in practice and do not apply to arbitrary privacy scenarios. Moreover, the privacy inference attack in the presence of semantic background knowledge is barely investigated. To address these shortcomings, in this work, we introduce knowledge graphs to explicitly express arbitrary prior belief of the attacker for any individual user. The processes of de-anonymization and privacy inference are accordingly formulated based on knowledge graphs. Our experiment on data of real social networks shows that knowledge graphs can power de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. This suggests the validity of knowledge graphs as a general effective model of attackers' background knowledge for social network attack and privacy preservation.
机译：出于研究目的和商业利益，社交网络数据被广泛共享，传输和发布，但是它引起了用户隐私的极大关注。即使总是删除用户的身份信息，攻击者仍可以借助辅助信息使用户匿名。为了防止反匿名攻击，已经提出了用于社交网络的各种隐私保护技术。但是，大多数现有方法都将特定的网络结构假设为背景知识，并将网络结构作为背景知识加以限制，并且忽略了攻击者的语义级别先验信念，这在实践中并不总是现实的，并且不适用于任意隐私情形。此外，几乎没有研究存在语义背景知识的情况下的隐私推断攻击。为了解决这些缺点，在这项工作中，我们引入知识图来明确表达攻击者对任何单个用户的任意先验信念。因此，基于知识图来制定去匿名化和隐私推断的过程。我们对真实社交网络数据的实验表明，知识图可以增强去匿名和推理攻击的能力，从而增加了隐私泄露的风险。这表明知识图作为攻击者背景知识用于社交网络攻击和隐私保护的通用有效模型的有效性。

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