Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model
Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model
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DOI：
10.1109/tdsc.2017.2697854
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发表时间：
2019-07
影响因子：
7.3
通讯作者：
Jianwei Qian;Xiangyang Li;Chunhong Zhang;Linlin Chen;Taeho Jung;Junze Han
中科院分区：
文献类型：
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作者：
Jianwei Qian;Xiangyang Li;Chunhong Zhang;Linlin Chen;Taeho Jung;Junze Han
出于研究和商业利益的目的，社交网络数据被广泛共享、传输和发布，但它也引起了人们对用户隐私的极大关注。即使用户的身份信息总是被删除，攻击者仍然可以借助辅助信息对用户进行去匿名化。为了防止去匿名化攻击，人们提出了各种针对社交网络的隐私保护技术。然而，现有的方法大多假设特定的和受限的网络结构作为背景知识，而忽略了攻击者语义层的先验信念，这在实践中并不总是现实的，不适用于任意的隐私场景。此外，在语义背景知识存在的情况下，隐私推理攻击也鲜有研究。为了解决这些不足，在本工作中，我们引入了知识图来显式地表示攻击者对任何个人用户的任意先验信念。在此基础上，建立了基于知识图的去匿名化和隐私推理过程。我们在真实社会网络数据上的实验表明，知识图可以增强去匿名化和推理攻击的能力，从而增加隐私泄露的风险。这表明知识图作为攻击者背景知识的一般有效模型对于社交网络攻击和隐私保护是有效的。
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.