Estimating age privacy leakage in online social networks
Estimating age privacy leakage in online social networks
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DOI：
10.1109/infcom.2012.6195711
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Ratan Dey;Cong Tang;K. Ross;Nitesh Saxena
中科院分区：
文献类型：
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作者：
Ratan Dey;Cong Tang;K. Ross;Nitesh Saxena
我们进行了一项大规模的研究，以量化Facebook的隐私泄露问题有多严重。作为一个案例研究，我们专注于估计出生年份，这是一个基本的人类属性，对许多人来说，是一个私人属性。具体来说，我们试图估计纽约市超过100万Facebook用户的出生年份。我们研究了几类用户的估计程序的准确性：（i）高度私人的用户，他们不公开他们的朋友列表;（ii）隐藏他们的出生年份，但公开他们的朋友列表的用户。为了估计Facebook用户的年龄，我们利用底层的社交网络结构设计了一个迭代算法，该算法基于朋友的年龄、朋友的年龄等来估计年龄。我们发现，对于大多数用户，包括隐藏朋友列表的高度隐私用户，估计年龄的误差只有几年。我们还向Facebook提出了一项具体建议，如果实施，将大大减少其服务中的隐私泄露。
We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public. To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service.