Online social networks (OSNs) have become one of the main communication channels in today's information society, and their emergence has raised new privacy concerns. The content uploaded to OSNs (such as pictures, status updates, comments) is by default available to the OSN provider, and often to other people to whom the user who uploaded the content did not intend to give access. A different class of concerns relates to sensitive information that can be inferred from the behavior of users. For example, the analysis of user interactions augments social network graphs with potentially privacy-sensitive details on the nature of social relations, such as the strength of user relationships. A solution to prevent such inferences is to automatically generate dummy interactions that obfuscate the real interactions between OSN users. Given an adversary that observes the obfuscated interactions, the goal is to prevent the adversary from recovering parameters of interest (e.g., relationships strength) that accurately describe the real user interactions. The design and evaluation of obfuscation strategies requires metrics that express the level of protection they would offer when deployed in a particular OSN with its underlying user interaction patterns. In this paper we propose mutual information as obfuscation metric. It measures the amount of information leaked by the (observable) obfuscated interactions in the system on the (concealed) real interactions between users. We show that the metric is suitable for comparing different obfuscation strategies, and flexible to accommodate different network topologies and user communication patterns. Obfuscation comes at the cost of network overhead, and the proposed metric contributes to enabling the optimization of strategies to achieve good levels of privacy protection at minimum overhead. We provide a detailed methodology to compute the metric and perform experiments that illustrate its suitability.
al. REF address a different, yet equally important, class of privacy issues, where sensitive information can be inferred from user behavior in the OSN.
A Metric to Evaluate Interaction Obfuscation in Online Social Networks
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Computer Science