The privacy issues in online social networks (OSNs) have been increasingly arousing the public awareness since it is possible for attackers to launch several kinds of attacks to obtain users' sensitive and private information by exploiting the massive data obtained from the networks. Even if users conceal their sensitive information, attackers can infer their secrets by studying the correlations between private and public information with background knowledge. To address these issues, the thesis focuses on the inference attack and its countermeasures. First, we study how to launch the inference attack to profile OSN users via relationships and network characteristics. Due to both user privacy concerns and unformatted textual information, it is quite difficult to build a completely labeled social network directly. However, both social relations and network characteristics can help attribute inference to profile OSN users. We propose several attribute inference models based on these two factors and implement them with Naive Bayes, Decision Tree, and Logistic Regression. Also, to study network characteristics and evaluate the performance of our proposed models, we use a well-labeled Google employee social network extracted from Google+ for inferring the social roles of Google employees. The experiment results demonstrate that the proposed models are effective in social role inference with Dyadic Label Model performing the best. Second, we model the general inference attack and formulate the privacy-preserving data sharing problem to defend against the attack. The optimization problem is to maximize the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficient privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) which can be solved with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones. Finally, we design a privacy protection authorization framework based on the OAuth 2.0 protocol. Many third-party services and applications have integrated the login services of popular social networking sites, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. However, due to the inference attack, it is still possible to infer users' secrets. Therefore, we embed our privacy-preserving data sharing algorithms in the implementation of OAuth 2.0 framework and propose RANPriv-OAuth2 to protect users' privacy from the inference attack. / Graduate

This thesis examines the multifarious presentation of one’s identity on a singular social network site through their usage of both “finsta” and “rinsta” accounts on Instagram. A rinsta is one’s primary and more public Instagram account. A finsta is a highly privatized secondary Instagram account that functions as a liminal space for users’ impression management engagements. In-depth interviews were conducted with participants who identified as having created a finsta account and thematic analysis was subsequently employed to understand how they conceptualized their motivations and behaviors within their constructed networks. It was found that users were motivated to create and maintain a finsta by a desire for privacy, social inclusion, and the freedom to generate content that would be considered socially unacceptable on rinsta. This socially unacceptable content was often humorous or emotionally expressive. Finstas are also characterized by in-depth communal interactions in comparison with the more superficial interactions on rinsta. / Includes bibliography. / Thesis (MA)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

One in seven people in the world use online social networking for a variety of purposes -- to keep in touch with friends and family, to share special occasions, to broadcast announcements, and more. The majority of society has been bought into this new era of communication technology, which allows everyone on the internet to share information with friends. Since social networking has rapidly become a main form of communication, holes in privacy have become apparent. It has come to the point that the whole concept of sharing information requires restructuring. No longer are online social networks simply technology available for a niche market; they are in use by all of society. Thus it is important to not forget that a sense of privacy is inherent as an evolutionary by-product of social intelligence. In any context of society, privacy needs to be a part of the system in order to help users protect themselves from others. This dissertation attempts to address the lack of privacy management in online social networks by designing models which understand the social science behind how we form social groups and share information with each other. Social relationship strength was modeled using activity patterns, vocabulary usage, and behavioral patterns. In addition, automatic configuration for default privacy settings was proposed to help prevent new users from leaking personal information. This dissertation aims to mobilize a new era of social networking that understands social aspects of human network, and uses that knowledge to honor users' privacy.

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This study addressed user perceptions of social network websites and music content based on uses and gratification. This method helped to analyze social network websites as a mass media channel and determine how websites were used by participants. Interviews for this research were conducted via online instant messenger tools including 23 participants from the Republic of Korea and the U.S. who were currently using MySpace and Cyworld via MySpace IM with Skype and NateOn messenger. All semi-structured in-depth interviews were conducted in English. The results centered on three main themes: (1) Benefits of using social network websites, (2) benefits of using music content on social network websites, and (3) motivations to purchase music content on social network websites. General implications of utilizing personal websites were discussed based on these results.

As the Internet has grown to represent arguably the largest “engineered” system on earth, network researchers have shown increasing interest in measuring this large-scale networked system. In the process, structures such as the physical Internet or the many different (logical) overlay networks that this physical infrastructure enables have been the focus of numerous studies. Many of these studies have been fueled by the ease of access to “big data”. Moreover, they benefited from advances in the study of complex networks. However, an important missing aspect in typical applications of complex network theory to the study of real-world distributed systems has been a general lack of attention to domain knowledge. On the one hand, missing or superficial domain knowledge can negatively affect the studies “input”; that is, limitations or idiosyncrasies of the measurement methods can render the resulting graphs difficult to interpret if not meaningless. On the other hand, lacking or insufficient domain knowledge can result in specious “output”; that is, popular graph abstractions of real-world systems are incapable of accounting for “details” that are important from an engineering perspective. In this thesis, we take a closer look at measurement-based characterization of a few real-world large-scale networked systems and focus on the role that domain knowledge plays in gaining a thorough understanding of these systems key properties and behavior. More specifically, we use domain knowledge to (i) design context-aware measurement strategies that capture the relevant information about the system of interest, (ii) analyze the captured view of the networked system baring in mind the abstraction imposed by the chosen graph representation, and (iii) scrutinize the results derived from the analysis of the graph-based representations by investigating the root causes underlying these findings. The main technical contribution of our work is twofolds. First, we establish concrete connections between the amount and level of domain knowledge needed and the quality of the measurements collected from networked systems. Second, we also provide concrete evidence for the role that domain knowledge plays in the analysis of views inferred from measurements collected from large-scale networked systems

This thesis investigates how quantitative user data, extracted from server logs, and clustering algorithms can be used to model and understand user-behavior. The thesis also investigates how the results compare to the more traditional method of qualitative user-behavior analysis through interviews and observations. The results show that clustering of all user data, as opposed to interviewing only a small subset of users, increases the reliability of findings. However, the quantitative method has a risk of missing important insights that can only be discovered through observation of the user. The conclusion drawn in this thesis is that a combination of both is necessary to truly understand the user-behavior. / Denna uppsats undersöker hur kvantitativ användardata, extraherad från serverloggar, och klustringsalgoritmer kan användas för att modellera och förstå användarbeteende. Uppsatsen undersöker också hur resultatet av denna metod skiljer sig från resultatet av den mer traditionella kvalitativa metoden för användarbeteendeanalys, baserad på intervjuer och observationer. Resultatet visar att klustring av all användardata, istället för att intervjuer med endast en delmängd av användarna, ökar pålitligheten i analysen. Dock visar resultatet också att den kvantitativa metoden riskerar att missa viktiga insikter som bara kan upptäckas med hjälp av observationer. Slutsatsen är att en kombination av både den kvantitativa och den kvalitativa metoden behövs för att helt kunna förstå användarbeteendet.

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Cette thèse utilise la théorie des jeux pour comprendre le comportement des usagers dans les réseaux sociaux. Trois problématiques y sont abordées: "Comment maximiser la popularité des contenus postés dans les réseaux sociaux?";" Comment modéliser la répartition des messages par sujets?";"Comment minimiser la propagation d’une rumeur et maximiser la diversité des contenus postés?". Après un état de l’art concernant ces questions développé dans le chapitre 1, ce travail traite, dans le chapitre 2, de la manière d’aborder l’environnement compétitif pour accroître la visibilité. Dans le chapitre 3, c’est le comportement des usagers qui est modélisé, en terme de nombre de messages postés, en utilisant la théorie des approximations stochastiques. Dans le chapitre 4, c’est une compétition pour être populaire qui est étudiée. Le chapitre 5 propose de formuler deux problèmes d’optimisation convexes dans le contexte des réseaux sociaux en ligne. Finalement, le chapitre 6 conclue ce manuscrit. / This Ph.D. is dedicated to the application of the game theory for the understanding of users behaviour in Online Social Networks. The three main questions of this Ph.D. are: " How to maximize contents popularity ? "; " How to model the distribution of messages across sources and topics in OSNs ? "; " How to minimize gossip propagation and how to maximize contents diversity? ". After a survey concerning the research made about the previous problematics in chapter 1, we propose to study a competition over visibility in chapter 2. In chapter 3, we model and provide insight concerning the posting behaviour of publishers in OSNs by using the stochastic approximation framework. In chapter 4, it is a popularity competition which is described by using a differential game formulation. The chapter 5 is dedicated to the formulation of two convex optimization problems in the context of Online Social Networks. Finally conclusions and perspectives are given in chapter 6.