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Identifier 000383546
Title Privacy-preserving twitter browsing through obfuscation
Alternative Title Διαφύλαξη της ιδιωτικότητας κατά την περιήγηση στο twitter μέσω της συγκάλυψης.
Thesis advisor Παπαδόπουλος, Παναγιώτης Ε.
Thesis advisor Μαρκάτος, Ευάγγελος
Reviewer Ιωαννίδης, Σωτήριος
Τραγανίτης Απόστολος
Abstract Over the past few years, microblogging social networking services have become a popular means of information sharing and communication. Although such services started as a convenient way of sharing small bits of information among friends, they 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 microblogging service enables users to receive interesting information in a timely manner, it may raise significant privacy concerns. 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, being property of the microblogging service, can be used for a variety of purposes, most of which 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) the actual interests of a user 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.
Language English
Subject Anonymity
K-subscription
Ανονυμία
Issue date 2014-03-28
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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