Security And Privacy In Social Networks And Big Data

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.

This book examines various topics and approaches related to the security and privacy in big data and cloud computing, where authors share their expertise in their respective chapters on a broad range of security and privacy challenges and state of the art solutions.

This book presents recent developments on the theoretical, algorithmic, and application aspects of Big Data in Complex and Social Networks. The book consists of four parts, covering a wide range of topics. The first part of the book focuses on data storage and data processing. It explores how the efficient storage of data can fundamentally support intensive data access and queries, which enables sophisticated analysis. It also looks at how data processing and visualization help to communicate information clearly and efficiently. The second part of the book is devoted to the extraction of essential information and the prediction of web content. The book shows how Big Data analysis can be used to understand the interests, location, and search history of users and provide more accurate predictions of User Behavior. The latter two parts of the book cover the protection of privacy and security, and emergent applications of big data and social networks. It analyzes how to model rumor diffusion, identify misinformation from massive data, and design intervention strategies. Applications of big data and social networks in multilayer networks and multiparty systems are also covered in-depth.

This book constitutes the proceedings of the 9th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2023, which took place in Canterbury, UK, in August 2023. The 10 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 29 submissions. They were organized in topical sections as follows: information abuse and political discourse; attacks; social structure and community; and security and privacy matters. Papers "Data Reconstruction Attack Against Principal Component Analysis" and "Edge local Differential Privacy for Dynamic Graphs" are published Open Access under the CC BY 4.0 License.

This book constitutes revised and selected papers from the 6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020, held in Tianjin, China, in September 2020. The 38 full papers presented in this volume were carefully reviewed and selected from a total of 111 submissions. The papers are organized according to the topical sections on big data security; social networks; privacy-preserving and security.

This book constitutes the proceedings of the 8th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2022, which took place in Xi'an, China, in October 2022. The 23 papers presented in this volume were carefully reviewed and selected from 103 submissions. The papers were evaluated on the basis of their significance, novelty, technical quality, as well as on their practical impact or their level of advancement of the field’s foundations. They were organized in topical sections as follows: Cryptography and its applications; Network security and privacy protection; Data detection; Blockchain and its applications.

This synthesis lecture provides a survey of work on privacy in online social networks (OSNs). This work encompasses concerns of users as well as service providers and third parties. Our goal is to approach such concerns from a computer-science perspective, and building upon existing work on privacy, security, statistical modeling and databases to provide an overview of the technical and algorithmic issues related to privacy in OSNs. We start our survey by introducing a simple OSN data model and describe common statistical-inference techniques that can be used to infer potentially sensitive information. Next, we describe some privacy definitions and privacy mechanisms for data publishing. Finally, we describe a set of recent techniques for modeling, evaluating, and managing individual users' privacy risk within the context of OSNs. Table of Contents: Introduction / A Model for Online Social Networks / Types of Privacy Disclosure / Statistical Methods for Inferring Information in Networks / Anonymity and Differential Privacy / Attacks and Privacy-preserving Mechanisms / Models of Information Sharing / Users' Privacy Risk / Management of Privacy Settings

This book constitutes revised and selected papers from the 5th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2019, held in Copenhagen, Denmark, in July 2019. The 18 full papers and 3 short papers presented in this volume were carefully reviewed and selected from a total of 76 submissions. The papers in the volume cover a broad range of topics on security in Internet-of-things, Social Networks, User Authentication, Algorithm design, Artificial Intelligence, and Big Data.