Analyzing and Securing Social Networks focuses on the two major technologies that have been developed for online social networks (OSNs): (i) data mining technologies for analyzing these networks and extracting useful information such as location, demographics, and sentiments of the participants of the network, and (ii) security and privacy technolo

Security and Privacy in Social Networks brings to the forefront innovative approaches for analyzing and enhancing the security and privacy dimensions in online social networks, and is the first comprehensive attempt dedicated entirely to this field. In order to facilitate the transition of such methods from theory to mechanisms designed and deployed in existing online social networking services, the book aspires to create a common language between the researchers and practitioners of this new area- spanning from the theory of computational social sciences to conventional security and network engineering.

The main target of this book is to raise the awareness about social networking systems design, implementation, security requirements, and approaches. The book entails related issues including computing, engineering, security, management, and organization policy. It interprets the design, implementation and security threats in the social networks and offers some solutions in this concern. It clarifies the authentication concept between servers to identity users. Most of the models that focus on protecting users’ information are also included. This book introduces the Human-Interactive Security Protocols (HISPs) efficiently. Presenting different types of the social networking systems including the internet and mobile devices is one of the main targets of this book. This book includes the social network performance evaluation metrics. It compares various models and approaches used in the design of the social networks. This book includes various applications for the use of the social networks in the healthcare, e-commerce, crisis management, and academic applications. The book provides an extensive background for the development of social network science and its challenges. This book discusses the social networks integration to offer online services, such as instant messaging, email, file sharing, transferring patients’ medical reports/images, location-based recommendations and many other functions. This book provides users, designers, engineers and managers with the valuable knowledge to build a better secured information transfer over the social networks. The book gathers remarkable materials from an international experts’ panel to guide the readers during the analysis, design, implementation and security achievement for the social network systems. In this book, theories, practical guidance, and challenges are included to inspire designers and researchers. The book guides the engineers, designers, and researchers to exploit the intrinsic design of the social network systems.

This book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling. The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.

The present volume provides a comprehensive resource for practitioners and researchers alike-both those new to the field as well as those who already have some experience. The work covers Social Network Analysis theory and methods with a focus on current applications and case studies applied in various domains such as mobile networks, security, machine learning and health. With the increasing popularity of Web 2.0, social media has become a widely used communication platform. Parallel to this development, Social Network Analysis gained in importance as a research field, while opening up many opportunities in different application domains. Forming a bridge between theory and applications makes this work appealing to both academics and practitioners as well as graduate students.

The social benefit derived from Online Social Networks (OSNs) can lure users to reveal unprecedented volumes of personal data to an online audience that is much less trustworthy than their offline social circle. Even if a user hides his personal data from some users and shares with others, privacy settings of OSNs may be bypassed, thus leading to various privacy harms such as identity theft, stalking, or discrimination. Therefore, users need to be assisted in understanding the privacy risks of their OSN profiles as well as managing their privacy settings so as to keep such risks in check, while still deriving the benefits of social network participation. This book presents to its readers how privacy risk analysis concepts such as privacy harms and risk sources can be used to develop mechanisms for privacy scoring of user profiles and for supporting users in privacy settings management in the context of OSNs. Privacy scoring helps detect and minimize the risks due to the dissemination and use of personal data. The book also discusses many open problems in this area to encourage further research.

The enormous success and diffusion that online social networks (OSNs) are encountering nowadays is vastly apparent. Users' social interactions now occur using online social media as communication channels; personal information and activities are easily exchanged both for recreational and business purposes in order to obtain social or economic advantages. In this scenario, OSNs are considered critical applications with respect to the security of users and their resources, for their characteristics alone: the large amount of personal information they manage, big economic upturn connected to their commercial use, strict interconnection among users and resources characterizing them, as well as user attitude to easily share private data and activities with strangers. In this book, we discuss three main research topics connected to security in online social networks: (i) trust management, because trust can be intended as a measure of the perception of security (in terms of risks/benefits) that users in an OSN have with respect to other (unknown/little-known) parties; (ii) controlled information sharing, because in OSNs, where personal information is not only connected to user profiles, but spans across users' social activities and interactions, users must be provided with the possibility to directly control information flows; and (iii) identity management, because OSNs are subjected more and more to malicious attacks that, with respect to traditional ones, have the advantage of being more effective by leveraging the social network as a new medium for reaching victims. For each of these research topics, in this book we provide both theoretical concepts as well as an overview of the main solutions that commercial/non-commercial actors have proposed over the years. We also discuss some of the most promising research directions in these fields.

This book focuses on three emerging research topics in mobile social networks (MSNs): privacy-preserving profile matching (PPM) protocols, privacy-preserving cooperative data forwarding (PDF) protocols, and trustworthy service evaluation (TSE) systems. The PPM helps two users compare their personal profiles without disclosing the profiles. The PDF helps users forward data to their friends via multiple cooperative relay peers while preserving their identity and location privacy. The TSE enables users to locally share service reviews on the vendors such that users receive more valuable information about the services not only from vendors but also from their trusted social friends. The authors address both theoretic and practical aspects of these topics by introducing the system model, reviewing the related works, and presenting the solutions. Security and Privacy for Mobile Social Networks further provides the security analysis and the performance evaluation based on real-trace simulations. It also summarizes the future research directions for this rapidly growing area of research. The book will be valuable for researchers and practitioners who work with mobile social networks, communication platforms, wireless communication techniques, and internet applications. "Suitable for any type of reader as an introduction to the topic... The chapters are well motivated and presented... It is recommended for researchers." -ACM Computing Reviews, 21 July 2014

Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.