Publication Type

Conference Proceeding Article

Publication Date



Most social networking platforms protect user's private information by limiting access to it to a small group of members, typically friends of the user, while allowing (virtually) everyone's access to the user's public data. In this paper, we exploit public data available on Facebook to infer users' undisclosed interests on their profile pages. In particular, we infer their undisclosed interests from the public data fetched using Graph APIs provided by Facebook. We demonstrate that simply liking a Facebook page does not corroborate that the user is interested in the page. Instead, we perform sentiment-oriented mining on various attributes of a Facebook page to determine the user's real interests. Our experiments conducted on over 34,000 public pages collected from Facebook and data from volunteers show that our inference technique can infer interests that are often hidden by users on their personal profile with moderate accuracy. We are able to disclose 22 interests of a user and find more than 80,097 users with at least 2 interests. We also show how this inferred information can be used to break a preference based backup authentication system.


social media, networking platforms, user public data, privacy, profile page information, Facebook, user real interests, inference of real interests


Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Management and Analytics


ASIA CCS'13: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security: May 8-10, 2013, Hangzhou, China

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City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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