This book covers and makes four major contributions: 1) analyzing and surveying the pros and cons of current approaches for identifying rumor sources on complex networks; 2) proposing a novel approach to identify rumor sources in time-varying networks; 3) developing a fast approach to identify multiple rumor sources; 4) proposing a community-based method to overcome the scalability issue in this research area. These contributions enable rumor source identification to be applied effectively in real-world networks, and eventually diminish rumor damages, which the authors rigorously illustrate in this book. In the modern world, the ubiquity of networks has made us vulnerable to various risks. For instance, viruses propagate throughout the Internet and infect millions of computers. Misinformation spreads incredibly fast in online social networks, such as Facebook and Twitter. Infectious diseases, such as SARS, H1N1 or Ebola, have spread geographically and killed hundreds of thousands people. In essence, all of these situations can be modeled as a rumor spreading through a network, where the goal is to find the source of the rumor so as to control and prevent network risks. So far, extensive work has been done to develop new approaches to effectively identify rumor sources. However, current approaches still suffer from critical weaknesses. The most serious one is the complex spatiotemporal diffusion process of rumors in time-varying networks, which is the bottleneck of current approaches. The second problem lies in the expensively computational complexity of identifying multiple rumor sources. The third important issue is the huge scale of the underlying networks, which makes it difficult to develop efficient strategies to quickly and accurately identify rumor sources. These weaknesses prevent rumor source identification from being applied in a broader range of real-world applications. This book aims to analyze and address these issues to make rumor source identification more effective and applicable in the real world. The authors propose a novel reverse dissemination strategy to narrow down the scale of suspicious sources, which dramatically promotes the efficiency of their method. The authors then develop a Maximum-likelihood estimator, which can pin point the true source from the suspects with high accuracy. For the scalability issue in rumor source identification, the authors explore sensor techniques and develop a community structure based method. Then the authors take the advantage of the linear correlation between rumor spreading time and infection distance, and develop a fast method to locate the rumor diffusion source. Theoretical analysis proves the efficiency of the proposed method, and the experiment results verify the significant advantages of the proposed method in large-scale networks. This book targets graduate and post-graduate students studying computer science and networking. Researchers and professionals working in network security, propagation models and other related topics, will also be interested in this book.

This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.

Both authors have taught the course of "Distributed Systems" for many years in the respective schools. During the teaching, we feel strongly that "Distributed systems" have evolved from traditional "LAN" based distributed systems towards "Internet based" systems. Although there exist many excellent textbooks on this topic, because of the fast development of distributed systems and network programming/protocols, we have difficulty in finding an appropriate textbook for the course of "distributed systems" with orientation to the requirement of the undergraduate level study for today's distributed technology. Specifically, from - to-date concepts, algorithms, and models to implementations for both distributed system designs and application programming. Thus the philosophy behind this book is to integrate the concepts, algorithm designs and implementations of distributed systems based on network programming. After using several materials of other textbooks and research books, we found that many texts treat the distributed systems with separation of concepts, algorithm design and network programming and it is very difficult for students to map the concepts of distributed systems to the algorithm design, prototyping and implementations. This book intends to enable readers, especially postgraduates and senior undergraduate level, to study up-to-date concepts, algorithms and network programming skills for building modern distributed systems. It enables students not only to master the concepts of distributed network system but also to readily use the material introduced into implementation practices.

This two volume set LNCS 8630 and 8631 constitutes the proceedings of the 14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers presented in the two volumes were selected from 285 submissions. The first volume comprises selected papers of the main conference and papers of the 1st International Workshop on Emerging Topics in Wireless and Mobile Computing, ETWMC 2014, the 5th International Workshop on Intelligent Communication Networks, IntelNet 2014, and the 5th International Workshop on Wireless Networks and Multimedia, WNM 2014. The second volume comprises selected papers of the main conference and papers of the Workshop on Computing, Communication and Control Technologies in Intelligent Transportation System, 3C in ITS 2014, and the Workshop on Security and Privacy in Computer and Network Systems, SPCNS 2014.

This two volume set LNCS 8630 and 8631 constitutes the proceedings of the 14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers presented in the two volumes were selected from 285 submissions. The first volume comprises selected papers of the main conference and papers of the 1st International Workshop on Emerging Topics in Wireless and Mobile Computing, ETWMC 2014, the 5th International Workshop on Intelligent Communication Networks, IntelNet 2014, and the 5th International Workshop on Wireless Networks and Multimedia, WNM 2014. The second volume comprises selected papers of the main conference and papers of the Workshop on Computing, Communication and Control Technologies in Intelligent Transportation System, 3C in ITS 2014, and the Workshop on Security and Privacy in Computer and Network Systems, SPCNS 2014.

This book constitutes the refereed proceedings of the 4th International Symposium on Cyberspace Safety and Security (CSS 2012), held in Melbourne, Australia, in December 2012. The 30 revised full papers presented together with 7 invited talks were carefully reviewed and selected from 105 submissions. The papers cover the following topics: mobile security, cyberspace attacks and defense, security application adn systems, network and cloud security, wireless security, security protocols and models.

There are many applications that require parallel and distributed processing to allow complicated engineering, business and research problems to be solved in a reasonable time. Parallel and distributed processing is able to improve company profit, lower costs of design, production, and deployment of new technologies, and create better business environments. The major lesson learned by car and aircraft engineers, drug manufacturers, genome researchers and other specialist is that a computer system is a very powerful tool that is able to help them solving even more complicated problems. That has led computing specialists to new computer system architecture and exploiting parallel computers, clusters of clusters, and distributed systems in the form of grids. There are also institutions that do not have so complicated problems but would like to improve profit, lower costs of design and production by using parallel and distributed processing on clusters. In general to achieve these goals, parallel and distributed processing must become the computing mainstream. This implies a need for new architectures of parallel and distributed systems, new system management facilities, and new application algorithms. This also implies a need for better understanding of grids and clusters, and in particular their operating systems, scheduling algorithms, load balancing, heterogeneity, transparency, application deployment, which is of the most critical importance for their development and taking them by industry and business.

Both authors have taught the course of "Distributed Systems" for many years in the respective schools. During the teaching, we feel strongly that "Distributed systems" have evolved from traditional "LAN" based distributed systems towards "Internet based" systems. Although there exist many excellent textbooks on this topic, because of the fast development of distributed systems and network programming/protocols, we have difficulty in finding an appropriate textbook for the course of "distributed systems" with orientation to the requirement of the undergraduate level study for today's distributed technology. Specifically, from - to-date concepts, algorithms, and models to implementations for both distributed system designs and application programming. Thus the philosophy behind this book is to integrate the concepts, algorithm designs and implementations of distributed systems based on network programming. After using several materials of other textbooks and research books, we found that many texts treat the distributed systems with separation of concepts, algorithm design and network programming and it is very difficult for students to map the concepts of distributed systems to the algorithm design, prototyping and implementations. This book intends to enable readers, especially postgraduates and senior undergraduate level, to study up-to-date concepts, algorithms and network programming skills for building modern distributed systems. It enables students not only to master the concepts of distributed network system but also to readily use the material introduced into implementation practices.

This book constitutes the refereed proceedings of the Second International Conference on Web-based learning, ICWL 2003, held in Melbourne, Australia in August 2003. The 50 revised full papers presented were carefully reviewed and selected from 118 submissions. The papers are organized in topical sections on Web-based learning environments, the virtual university, paedagoical issues, multimedia-based e-learning, interactivity, intelligence in online education, innovative curricula in e-learning, e-learning solutions, and computer supported cooperative learning.

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

This book constitutes the proceedings of the 20th International Conference on Advances in Web-Based Learning, ICWL 2021, which was held in Macau, China, in November 2021. The papers included in this volume deal with multiple topics, from algorithms to systems and applications and are organized in 3 tracks: Online learning methodologies, trust, and analysis; Online learning environment with tools; Online learning privacy issues and special tools.

This brief presents emerging and promising communication methods for network reliability via delay tolerant networks (DTNs). Different from traditional networks, DTNs possess unique features, such as long latency and unstable network topology. As a result, DTNs can be widely applied to critical applications, such as space communications, disaster rescue, and battlefield communications. The brief provides a complete investigation of DTNs and their current applications, from an overview to the latest development in the area. The core issue of data forward in DTNs is tackled, including the importance of social characteristics, which is an essential feature if the mobile devices are used for human communication. Security and privacy issues in DTNs are discussed, and future work is also discussed.

This book provides a comprehensive study of the state of the art in location privacy for mobile applications. It presents an integrated five-part framework for location privacy research, which includes the analysis of location privacy definitions, attacks and adversaries, location privacy protection methods, location privacy metrics, and location-based mobile applications. In addition, it analyses the relationships between the various elements of location privacy, and elaborates on real-world attacks in a specific application. Furthermore, the book features case studies of three applications and shares valuable insights into future research directions. Shedding new light on key research issues in location privacy and promoting the advance and development of future location-based mobile applications, it will be of interest to a broad readership, from students to researchers and engineers in the field.