Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks
| Autor: | Kang Li, Calton Pu, Danesh Irani, Steve Webb |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Information privacy
Social network Computer Networks and Communications business.industry Computer science Privacy policy Internet privacy Computer security computer.software_genre Social web Electronic mail Information leakage business Personally identifiable information computer Vulnerability (computing) |
| Zdroj: | IEEE Internet Computing. 15:13-19 |
| ISSN: | 1089-7801 |
| DOI: | 10.1109/mic.2011.25 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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