Optimal Active social Network De-anonymization Using Information Thresholds
Optimal Active social Network De-anonymization Using Information Thresholds
复制标题
使用信息阈值的最佳主动社交网络去匿名化
DOI：
10.1109/isit.2018.8437739
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
E. Erkip
中科院分区：
文献类型：
--
作者：
Farhad Shirani;S. Garg;E. Erkip
在本文中，去匿名的互联网用户主动查询他们的组成员在社交网络被认为是。匿名受害者访问攻击者的网站，攻击者使用受害者的浏览器历史记录来查询她的社交媒体活动，以便使用最少的查询次数进行去匿名化。一个随机模型的问题被认为是攻击者有部分先验知识的组成员关系图，并收到嘈杂的响应，其实时查询。受害者的身份被假定为随机选择的基础上，一个给定的分布模型的用户访问恶意网站的风险。提出了一种基于信息阈值的去匿名化算法，并分析了其在有限和渐近大社会网络状态下的性能。此外，提供了一个匡威的结果，证明了所提出的攻击策略的最优性。
In this paper, de-anonymizing internet users by actively querying their group memberships in social networks is considered. An anonymous victim visits the attacker's website, and the attacker uses the victim's browser history to query her social media activity for the purpose of de-anonymization using the minimum number of queries. A stochastic model of the problem is considered where the attacker has partial prior knowledge of the group membership graph and receives noisy responses to its real-time queries. The victim's identity is assumed to be chosen randomly based on a given distribution which models the users' risk of visiting the malicious website. A de-anonymization algorithm is proposed which operates based on information thresholds and its performance both in the finite and asymptotically large social network regimes is analyzed. Furthermore, a converse result is provided which proves the optimality of the proposed attack strategy.