K-subscription: Privacy-preserving microblogging browsing through obfuscation

Over the past few years, microblogging social networking services have become a popular means for information sharing and communication. Besides sharing information among friends, such services are currently being used by artists, politicians, news channels, and information providers to easily communicate with their constituency. Even though following specific channels on a mi-croblogging service enables users to receive interesting information in a timely manner, it may raise significant privacy concerns as well. For example, the microblogging service is able to observe all the channels that a particular user follows. This way, it can infer all the subjects a user might be interested in and generate a detailed profile of this user. This knowledge can be used for a variety of purposes that are usually beyond the control of the users. To address these privacy concerns, we propose k-subscription: an obfuscation-based approach that enables users to follow privacy-sensitive channels, while, at the same time, making it difficult for the microblogging service to find out their actual interests. Our method relies on obfuscation: in addition to each privacy-sensitive channel, users are encouraged to randomly follow k-1 other channels they are not interested in. In this way (i) their actual interests are hidden in random selections, and (ii) each user contributes in hiding the real interests of other users. Our analysis indicates that k-subscription makes it difficult for attackers to pinpoint a user's interests with significant confidence. We show that this confidence can be made predictably small by slightly adjusting k while adding a reasonably low overhead on the user's system.