curso: protect yourself from curse of attribute inference: a social network privacy-analyzer

Authors:
Eunsu Ryu;Yao Rong;Jie Li;Ashwin Machanavajjhala
Affiliations:
Duke University, Durham, NC;Duke University, Durham, NC;Duke University, Durham, NC;Duke University, Durham, NC
Venue:
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Year:
2013

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Abstract

While social networking platforms allow users to control how their private information is shared, recent research has shown that a user's sensitive attribute can be inferred based on friendship links and group memberships, even when the attribute value is not shared with anyone else. Thus, existing access control mechanisms are unable to protect against such privacy breaches. Our research goal is to develop tools that help a user Alice be aware of privacy breaches via attribute inference. In this paper, we specifically focus on two problems: (a) whether Alice's sensitive attribute can be inferred based on public information in Alice's neighborhood, and (b) whether making Alice's sensitive attribute public leads to the disclosure of sensitive information of another user Bob in Alice's neighborhood. We propose three algorithms to detect the aforementioned privacy breaches. We limit our scope to the one-hop neighbors of Alice -- information that is visible to an app that can be executed on behalf of Alice. Our results indicate that analyzing local networks is sufficient to extract a significant amount of information about most users.