Algorithms for Data and Computation Privacy

This book provides state-of-the-art algorithms for data and computation privacy, focusing on searchable symmetric encryption and privacy-preserving multi-party computation. It also covers breaking privacy and designing algorithms to counter privacy attacks, with examples of well-designed differential privacy algorithms. With the rise of cloud computing, preserving data and computation privacy is crucial, and this book is aimed at database engineers, cloud computing engineers, and researchers in this field.

Format: Paperback / softback
Length: 404 pages
Publication date: 30 November 2021
Publisher: Springer Nature Switzerland AG

This comprehensive text delves into the realm of state-of-the-art algorithms for data and computation privacy, offering a comprehensive exploration of its core concepts. It primarily focuses on searchable symmetric encryption algorithms and privacy-preserving multi-party computation algorithms, providing readers with a deep understanding of these critical technologies. Moreover, the book delves into the realm of breaking privacy, offering insightful explanations and intuitions on how to design algorithms that can counter privacy attacks. Additionally, this text includes well-designed differential privacy algorithms, which are gaining increasing importance in the context of preserving privacy in the age of big data.

The rise of cloud computing has brought about a paradigm shift in the way data and computing services are delivered. In this computing landscape, privacy-sensitive data is often stored at parties that may not fully trust each other, and privacy-sensitive computation is performed with parties that may not fully trust the other parties involved. This underscores the critical importance of preserving data privacy and computation privacy in these scenarios.

In recent years, there has been a growing awareness and concern about privacy among users in the digital world. The Facebook–Cambridge Analytical data scandal and the implementation of the General Data Protection Regulation by the European Union have further heightened these concerns. This book is specifically designed to cater to the needs of database engineers, cloud computing engineers, and researchers working in the field of data and computation privacy. It offers a valuable resource for advanced-level students studying computer science and electrical engineering, providing them with a comprehensive and up-to-date understanding of the latest algorithms and techniques in this field.

The book is organized into five chapters, each covering a different aspect of data and computation privacy. Chapter 1 provides an introduction to the topic, laying the foundation for the subsequent chapters. Chapter 2 explores searchable symmetric encryption algorithms, which are used to encrypt data so that only authorized parties can access it. Chapter 3 focuses on privacy-preserving multi-party computation algorithms, which enable multiple parties to perform computation on shared data without revealing their individual data. Chapter 4 delves into the realm of breaking privacy, discussing techniques and algorithms that can be used to compromise the privacy of data. Chapter 5 introduces differential privacy algorithms, which are designed to protect the privacy of data while allowing statistical analysis and aggregation.

Throughout the book, the authors employ a clear and concise writing style, making the content accessible to a wide range of readers. Each chapter includes detailed explanations, examples, and exercises to reinforce the concepts presented. Additionally, the book includes a comprehensive bibliography that provides further reading for those interested in exploring the topic in greater depth.

In conclusion, this book is a valuable resource for anyone interested in data and computation privacy. It offers a comprehensive coverage of state-of-the-art algorithms, insights into privacy-breaking techniques, and practical applications of differential privacy algorithms. Whether you are a professional working in the field or an advanced-level student seeking to expand your knowledge, this book will provide you with the necessary tools and knowledge to excel in this rapidly evolving area.

Weight: 646g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030588984
Edition number: 1st ed. 2021