Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks

被引：50

作者：

Irani, Danesh ^{[1
]}

Webb, Steve

Pu, Calton ^{[2
]}

Li, Kang ^{[3
]}

机构：

[1] Georgia Inst Technol, Comp Sci Program, Atlanta, GA 30332 USA

[2] Georgia Inst Technol, Sch Comp Sci, Atlanta, GA 30332 USA

[3] Univ Georgia, Athens, GA 30602 USA

来源：

IEEE INTERNET COMPUTING | 2011年 / 15卷 / 03期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

personal information leakage; security and privacy; Social networks;

D O I：

10.1109/MIC.2011.25

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

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.

引用

页码：13 / 19

页数：7

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