Security and Privacy in Federated Learning / by Shui Yu, Lei Cui.

Format:

Book

Author/Creator:

Yu, Shui, Author.

Cui, Lei, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Digital Privacy and Security, 2731-9938

Language:

English

Subjects (All):

Data protection.

Data protection-Law and legislation.

Artificial intelligence.

Data and Information Security.

Privacy.

Artificial Intelligence.

Local Subjects:

Data and Information Security.

Privacy.

Artificial Intelligence.

Physical Description:

1 online resource (XII, 133 pages) : 1 illustrations

Edition:

1st ed. 2023.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2023.

System Details:

text file PDF

Summary:

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.

Contents:

Chapter 1. Introduction of Federated Learning

Chapter 2. Inference Attacks and Counter Attacks in Federated Learning

Chapter 3. Poisoning Attacks and Counter Attacks in Federated Learning

Chapter 4. GAN Attacks and Counter Attacks in Federated Learning

Chapter 5. Differential Privacy in Federated Learning

Chapter 6. Secure Multi-Party Computation in Federated Learning

Chapter 7. Secure Data Aggregation in Federated Learning

Chapter 8. Anonymous Communication and Shuffle Model in Federated Learning

Chapter 9. The Future Work.

Other Format:

Printed edition:

ISBN:

978-981-19-8692-5

9789811986925

Access Restriction:

Restricted for use by site license.