Privacy-preserving Computing: for Big Data Analytics and AI

Privacy-preserving computing is a method that uses big data while protecting users' personal information. It covers cryptographic techniques, differential privacy, and federated learning and has applications in data analytics and AI.

Format: Hardback
Length: 271 pages
Publication date: 16 November 2023
Publisher: Cambridge University Press

Privacy-preserving computing is a cutting-edge field that strives to safeguard users' personal information while harnessing the immense potential of big data. This comprehensive introduction, designed for students, researchers, and industry professionals, presents the latest advancements in the field over the past four decades. The book offers a cohesive and systematic exploration of privacy-preserving computing, providing a valuable resource for those seeking to understand and apply these techniques in real-world data analytics and AI applications.

At the heart of privacy-preserving computing lies the use of cryptographic techniques, such as secret sharing, homomorphic encryption, and oblivious transfer. These techniques enable the secure processing and analysis of sensitive data while maintaining privacy guarantees. The book expands its focus to encompass more widely applicable techniques, including differential privacy, trusted execution environments, and federated learning.

Throughout the book, practical examples and case studies are provided to illustrate the application of privacy-preserving computing in various domains, including statistics, database queries, machine learning, finance, online advertising, and healthcare. These examples showcase how privacy-preserving techniques can be integrated into existing systems and processes to protect user privacy without compromising data utility.

The book concludes with a vision for the future of privacy-preserving computing, highlighting emerging trends and research directions. It discusses the challenges and opportunities associated with the growing demand for privacy-sensitive data analysis and the need for interdisciplinary collaboration to address these challenges.

In summary, privacy-preserving computing is a vital field that empowers users to leverage big data while ensuring their privacy. This introduction provides a comprehensive and accessible guide to the latest techniques and applications, making it an essential resource for students, researchers, and industry practitioners alike.

Weight: 526g
Dimension: 157 x 236 x 21 (mm)
ISBN-13: 9781009299510