Privacy-preserving Computing: for Big Data Analytics and AI

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Kai Chen (Hong Kong University of Science and Technology) Qiang Yang (WeBank and Hong Kong University of Science and Technology)

Privacy-preserving Computing

for Big Data Analytics and AI

Kai Chen (Hong Kong University of Science and Technology), Qiang Yang (WeBank and Hong Kong University of Science and Technology)

$199.95 $159.62

Hardback

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English

Cambridge University Press
16 November 2023

Health & safety aspects of computing; Ethical & social aspects of computing; Digital lifestyle; Privacy & data protection; Artificial intelligence

Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.

By: Kai Chen (Hong Kong University of Science and Technology), Qiang Yang (WeBank and Hong Kong University of Science and Technology)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 155mm, Spine: 21mm
Weight: 530g
ISBN: 9781009299510
ISBN 10: 1009299514
Pages: 271
Publication Date: 16 November 2023
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active

Kai Chen is Professor at the Department of Computer Science and Engineering of the Hong Kong University of Science and Technology, where he leads the Intelligent Networking and Systems (iSING) Lab and the WeChat-HKUST Joint Lab on Artificial Intelligence Technology. His research interests include data center networking, high-performance networking, machine learning systems, and hardware acceleration. Qiang Yang is Chief Ai Officer at Webank and Professor Emeritus at the Department of Computer Science and Engineering of the Hong Kong University of Science and Technology. He is an AAAI, ACM, and IEEE Fellow and Fellow of the Canadian Royal Society. He has authored books such as 'Intelligent Planning,' 'Crafting Your Research Future,' 'Transfer Learning,' and 'Federated Learning.' His research interests include artificial intelligence, machine learning and data mining, automated planning, transfer learning, and federated learning.

Reviews for Privacy-preserving Computing: for Big Data Analytics and AI

'While we are witnessing revolutionary changes in AI technology empowered by deep learning and large-scale computing, data privacy for trusted machine learning plays an essential role in safe and reliable AI deployment. This book introduces fundamental concepts and advanced techniques for privacy-preserving computation for data mining and machine learning, which serve as a foundation for safe and secure AI development and deployment.' Pin-Yu Chen, IBM Research