当你所有的朋友都为我们所知时，
All liaisons are dangerous when all your friends are known to us
摘要：
在线社交网络（OSN）被全世界数百万用户使用。从学术上讲，对OSN进行的人口统计学研究的有用性几乎没有疑问，因此，从小的标记样本中标记未知用户的方法非常有用。然而，从公众的角度来看，这可能是一个严重的隐私问题。因此，这两个主题在本文中解决：首先，一个新的算法来执行用户分析在社交网络中描述，其性能的报告和讨论。其次，实验-进行的信息通常被认为是敏感的-揭示，仅仅公布一个人的联系人的隐私是有风险的，因此，措施，以尽量减少由于社交图数据采掘的隐私泄露概述。
英文摘要：
Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user profiling in social networks is described, and its performance is reported and discussed. Secondly, the experiments --conducted on information usually considered sensitive-- reveal that by just publicizing one's contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
