论文标题
在线社交网络之间的个人资料匹配
Profile Matching Across Online Social Networks
论文作者
论文摘要
在这项工作中，我们研究了由于跨在线社交网络（OSN）匹配的个人资料匹配引起的隐私风险，其中使用有关它们的辅助信息将OSN用户的匿名配置文件与其真实身份匹配。我们考虑用户公开共享的不同属性。这些属性包括强大的标识符，例如用户名和弱标识符，例如兴趣或不同平台中用户不同帖子之间的情感变化。我们研究将这些属性的不同组合与配置文件匹配的效果，以便以广泛的方式显示隐私威胁。提出的框架主要依赖于机器学习技术和优化算法。我们在三个数据集（Twitter -Foursquare，Google+ -twitter和Flickr）上评估了建议的框架，并通过使用公开共享的属性和/或OSN的基础图形结构来展示不同OSN中用户的配置文件。我们还表明，与依赖机器学习技术的最新技术相比，所提出的框架特别提供了更高的精度值。我们认为，这项工作将是为OSN用户构建工具以了解其公开份额的隐私风险的宝贵步骤。
In this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. We consider different attributes that are publicly shared by users. Such attributes include both strong identifiers such as user name and weak identifiers such as interest or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to profile matching in order to show the privacy threat in an extensive way. The proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on three datasets (Twitter - Foursquare, Google+ - Twitter, and Flickr) and show how profiles of the users in different OSNs can be matched with high probability by using the publicly shared attributes and/or the underlying graphical structure of the OSNs. We also show that the proposed framework notably provides higher precision values compared to state-of-the-art that relies on machine learning techniques. We believe that this work will be a valuable step to build a tool for the OSN users to understand their privacy risks due to their public sharings.
