Lei Cui – författare

Robots interact with the world through curves and surfaces — the subjects of study in differential geometry. This book applies the moving-frame method, developed extensively by Élie Cartan, and the adjoint approach, conceived by Ernesto Cesàro, to study the kinematics of two surfaces subject to rolling contact and sliding–rolling contact to demonstrate the applications in robotic in-hand manipulation.Firstly, it explores two surfaces, and the geometry of both surfaces comes into play. Secondly, the book focuses on the geometry of the two surfaces within the encompassing space (extrinsic) rather than within the surfaces (intrinsic) because the book is concerned with the kinematics of one surface in three-dimensional Euclidean space — the real world. The book then concludes by applying this approach in robotic in-hand manipulation in the last chapter.

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.

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.

In this book, the authors highlight the latest research findings on the security and privacy of federated learning systems. The main attacks and counterattacks in this booming field are presented to readers in connection with inference, poisoning, generative adversarial networks, differential privacy, secure multi-party computation, homomorphic encryption, and shuffle, respectively.   The book offers an essential overview for researchers who are new to the field, while also equipping them to explore this “uncharted territory.” For each topic, the authors first present the key concepts, followed by the most important issues and solutions, with appropriate references for further reading. The book is self-contained, and all chapters can be read independently. It offers a valuable resource for master’s students, upper undergraduates, Ph.D. students, and practicing engineers alike.