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Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks

机译:建模来自多个在线社交网络的意外个人信息泄漏

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摘要

Most people have multiple accounts on different social networks. Because these networks offer various levels of privacy protection, the weakest privacy policies in the social network ecosystem determine how much personal information is disclosed online. A new information leakage measure quantifies the information available about a given user. Using this measure makes it possible to evaluate the vulnerability of a user's social footprint to two known attacks: physical identification and password recovery. Experiments show the measure's usefulness in quantifying information leakage from publicly crawled information and also suggest ways of better protecting privacy and reducing information leakage in the social Web.
机译:大多数人在不同的社交网络上有多个帐户。由于这些网络提供各种级别的隐私保护,因此社交网络生态系统中最弱的隐私策略决定了在线公开多少个人信息。一种新的信息泄漏措施可量化有关给定用户的可用信息。使用此措施可以评估用户的社会足迹对两种已知攻击的脆弱性:物理标识和密码恢复。实验表明,该措施在量化从公共爬网信息中泄漏信息的有用性,还提出了更好地保护隐私并减少社交网络中信息泄漏的方法。

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