Towards Understanding Privacy Risks in Online Social Networks

The increasing popularity of online social networks (OSNs) has led themto become an important part of people's everyday communication. With millions of individuals who use OSNs to share all kinds of contents, privacy concerns of how all this content is managed have arisen. Social networks are used to share a variety of information, ranging from trivial messages to compromising pictures, music recommendations or information about the status of the public transport. Regardless of the specificpiece of information shared, users expect to control how and where thisinformation is disclosed by configuring the privacy options of their profile within the social network. OSNs are characterized for allowing users to create explicit relationships between them. For this reason, apart from all personal data that users share in the network, user connections are added to the set of sensitive information that OSNs contain. These relationships can be considered sensitive data by themselves. However, these relationships are extremely important not only for the direct information they disclose but also for the structural information they provide. Relationships can be used to infer information about users byanalyzing the network structure they form. The main objective ofthe proposed dissertation is to improve the current situation of onlinesocial network users as to the preservation of their privacy concerns. We will focus on the privacy of the relationships between users, that isthe structural information of these networks, since it is a newly established field in which there is still much work to do. We aim to address the general goal by approaching two different data collection scenarios: obtaining data using web crawlers, and acquiring it directly from the OSN provider. In this line, we concrete the general goal in the following sub-objectives: 1. To study the techniques that allow to infer private information about users from the relationships that they form in OSNs. 2. To examine the techniques and algorithms that allow to obtain OSN relationships from data publicly available on the Internet. 3. To propose local mechanisms that users can put in practice to minimize the information that can be obtained from their relationships in OSNs. 4. To analyze the existing techniques for network data anonymization and its effectiveness in different scenarios. 5. To propose new graph anonymization mechanisms that improve the existing proposals.Abstract Acknowledgements Contents List of Figures List of Tables Abbreviations 1 Introduction 2 Preliminary concepts 3 Related work 4 When multiple autonomous users disclose another individual’s information 5 Crawler scheduling and its privacy implications 6 OSN crawling schedulers and their implications on k-plexes detection 7 On improving classification of interlinked entities using only the network structure 8 Improving relational classification using link prediction techniques 9 Towards inferring communication patterns in online social networks 10 Conclusions A OSN crawler implementation Bibliographynrpages: 234status: publishe