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Synopsis
Systematically introduces privacy-preserving computing techniques and practical applications for students, researchers, and practitioners.
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About the Authors
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.
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- PublisherCambridge University Press
- Publication date2023
- ISBN 10 1009299514
- ISBN 13 9781009299510
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages271
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Privacy-preserving Computing : For Big Data Analytics and Ai
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Privacy-preserving Computing
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Hardcover. Condition: new. Hardcover. 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. Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities offered by big data. This practical introduction for students, researchers, and industry practitioners presents a systematic tour of recent advances in privacy-preserving methods for real-world problems in analytics and AI. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781009299510
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Privacy-preserving Computing: for Big Data Analytics and AI
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Privacy-preserving Computing : For Big Data Analytics and Ai
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