Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks

Authors:
Danesh Irani;Steve Webb;Kang Li;Calton Pu
Affiliations:
Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;University of Georgia
Venue:
IEEE Internet Computing
Year:
2011

Citing 0
Cited 7

Is more always merrier?: a deep dive into online social footprints

Proceedings of the 2012 ACM workshop on Workshop on online social networks
Syntactic analysis for monitoring personal information leakage on social network services: a case study on twitter

ICT-EurAsia'13 Proceedings of the 2013 international conference on Information and Communication Technology
Music similarity and retrieval

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Mosaic: quantifying privacy leakage in mobile networks

Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Game theoretic attack analysis in online social network (OSN) services

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Account Reachability: A Measure of Privacy Risk for Exposure of a User's Multiple SNS Accounts

Proceedings of International Conference on Information Integration and Web-based Applications & Services
Escaping the Big Brother: An empirical study on factors influencing identification and information leakage on the Web

Journal of Information Science

Quantified Score

Hi-index	0.00

Visualization

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.