Learning based access control in online social networks

Mohamed Shehab, Gorrell Cheek, Hakim Touati, Anna Squicciarini, Pau Chen Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


Online social networking sites are experiencing tremendous user growth with hundreds of millions of active users. As a result, there is a tremendous amount of user profile data online, e.g., name, birthdate, etc. Protecting this data is a challenge. The task of access policy composition is a tedious and confusing effort for the average user having hundreds of friends. We propose an approach that assists users in composing and managing their access control policies. Our approach is based on a supervised learning mechanism that leverages user provided example policy settings as training sets to build classifiers that are the basis for auto-generated policies. Furthermore, we provide mechanisms to enable users to fuse policy decisions that are provided by their friends or others in the social network. These policies then regulate access to user profile objects. We implemented our approach and, through extensive experimentation, prove the accuracy of our proposed mechanisms.

Original languageEnglish (US)
Title of host publicationProceedings of the 19th International Conference on World Wide Web, WWW '10
Number of pages2
StatePublished - Jul 20 2010
Event19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States
Duration: Apr 26 2010Apr 30 2010


Other19th International World Wide Web Conference, WWW2010
Country/TerritoryUnited States
CityRaleigh, NC

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications


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