DocumentCode :
1431734
Title :
Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks
Author :
Irani, Danesh ; Webb, Steve ; Pu, Calton ; Li, Kang
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA, USA
Volume :
15
Issue :
3
fYear :
2011
Firstpage :
13
Lastpage :
19
Abstract :
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.
Keywords :
data privacy; social networking (online); multiple online social networks; privacy protection; publicly crawled information; social Web; unintended personal information leakage; user social footprint; Aggregates; Authentication; Electronic mail; Modeling; Online services; Privacy; Social network services; Social networks; personal information leakage; security and privacy;
fLanguage :
English
Journal_Title :
Internet Computing, IEEE
Publisher :
ieee
ISSN :
1089-7801
Type :
jour
DOI :
10.1109/MIC.2011.25
Filename :
5696719
Link To Document :
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