Youyang Qu – författare

This book constitutes the refereed post-conference proceedings of the 17th EAI International Conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TridentCom 2022, which was held in Melbourne, Australia, in November 23-25,2022. The 11 full papers were selected from 30 submissions and deal the emerging technologies of big data, cyber-physical systems and computer communications. The papers are grouped in thematical sessions on network security; network communication; network services; mobile and ad hoc networks; blockchain; machine learning.

Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm.

Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm.

This book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy.  Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic.In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection.  Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets.The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.

This book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy.  Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic.In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection.  Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets.The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.