论文标题
关于您的一切：在线社交网络中友谊推断的多模式方法
Everything About You: A Multimodal Approach towards Friendship Inference in Online Social Networks
论文作者
论文摘要
在线社交网络（OSN）隐私方面的大多数先前工作都集中在使用一种类型的信息来推断另一种类型的信息或仅使用静态配置文件数据（例如用户名，个人资料图片或家庭位置）的限制情况。但是，如今，用户的多媒体足迹已变得极为多样。实际上，对手会利用随着时间的推移获得的所有类型的信息，以实现其目标。在本文中，我们通过共同利用长期多模式信息来分析OSN隐私。我们特别关注社会关系的推断。我们考虑用户共享的五个流行组成部分，即图像，主题标签，标题，地理位置和已出版的友谊。对实际OSN数据集的大规模评估表明，虽然我们的单差攻击实现了强烈的预测，但我们的多模式攻击导致AUC（ROC曲线下的区域）高于0.9的AUC表现更强。我们的结果强调了在一个用户越来越多样化的时代，需要多模式混淆方法来保护隐私。
Most previous works in privacy of Online Social Networks (OSN) focus on a restricted scenario of using one type of information to infer another type of information or using only static profile data such as username, profile picture or home location. However the multimedia footprints of users has become extremely diverse nowadays. In reality, an adversary would exploit all types of information obtainable over time, to achieve its goal. In this paper, we analyse OSN privacy by jointly exploiting longterm multimodal information. We focus in particular on inference of social relationships. We consider five popular components of posts shared by users, namely images, hashtags, captions, geo-locations and published friendships. Large scale evaluation on a real-world OSN dataset shows that while our monomodal attacks achieve strong predictions, our multimodal attack leads to a stronger performance with AUC (area under the ROC curve) above 0.9. Our results highlight the need for multimodal obfuscation approaches towards protecting privacy in an era where multimedia footprints of users get increasingly diverse.
