De-Anonymizing Users Across Heterogeneous Social Computing Platforms
De-Anonymizing Users Across Heterogeneous Social Computing Platforms
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DOI：
10.1609/icwsm.v7i1.14456
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
M. Korayem;David J. Crandall
中科院分区：
文献类型：
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作者：
M. Korayem;David J. Crandall
许多人使用多个在线和社交计算平台，并根据网站的上下文和类型选择共享不同数量的个人信息。例如，人们可能愿意在Facebook等网站上分享个人身份信息（包括他们的真实的姓名和出生日期），但可能会在陌生人广泛浏览的约会网站上保留他们的身份。我们研究了用户在不同网站上的活动模式的微妙相关性可以用来推断两个帐户对应于同一个人的程度。我们研究了各种特征，包括时间访问模式、文本内容、地理标签和社交联系的相似性，发现即使是非常微弱的信号也会产生令人惊讶的准确去匿名化结果。
Many people use multiple online and social computing platforms, and choose to share varying amounts of personal information about themselves depending on the context and type of site. For example, people may be willing to share personally-identifiable details (including their real name and date of birth) on a site like Facebook, but may withhold their identity on a dating site that may be widely viewed by strangers. We study the extent to which subtle correlations in a user's activity patterns across different sites may be used to infer that two accounts correspond to the same person. We study a variety of features, including similarity of temporal access patterns, textual content, geo-tags, and social connections, finding that even very weak signals yield surprisingly accurate de-anonymization results.