We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks.
To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users, via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.
Maximizing Acceptance Probability for Active Friending in On-Line Social Networks
好友推荐成功地促进了在线社交网络的爆炸式增长。当今大多数交友推荐服务旨在支持被动交友，其中用户被动地从推荐的候选者中选择交友目标。在本文中，我们提倡推荐支持主动交友，用户主动指定一个交友目标。据我们所知，在现有的在线社交网络服务中，还没有探索设计用于为用户系统地接近他的交友目标提供指导的推荐。为了最大限度地提高概率，交友目标将接受邀请，从用户，我们制定了一个新的优化问题，即\emph{Acceptance Probability Maximization (APM)}，并开发了一个多项式时间的算法，称为\emph{Selective Invitation with Tree and In-Node Aggregation (SITINA)}，找到最佳的解决方案。我们实现了一个积极的交友服务与SITINA在Facebook上验证我们的想法。我们的用户研究和实验结果表明，SITINA在解决方案的质量有效地优于人工选择和基线方法。
Incorporating Like-Minded Peers to Overcome Friend Data Sparsity in Session-Based Social Recommendations
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；College of Medicine, University of Florida（佛罗里达大学医学院）；School of Engineering and Computer Science, Victoria University of Wellington（惠灵顿维多利亚大学工程与计算机科学学院）；School of Computer Science, Fudan University（复旦大学计算机学院）
