Privacy-preserving Computing
for Big Data Analytics and AI
- Authors:
- Kai Chen, Hong Kong University of Science and Technology
- Qiang Yang, WeBank and Hong Kong University of Science and Technology
- Date Published: November 2023
- availability: Available
- format: Hardback
- isbn: 9781009299510
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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.
Read more- Provides a much-needed systematic overview of privacy-preserving computing techniques
- Explores practical applications of the techniques covered to allow readers to see them in practice
- Contains practical guidance and real-world case studies useful to industry practitioners
Reviews & endorsements
'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
See more reviews'Recommended to all readers interested in privacy-preserving computing.' C. Tappert, CHOICE
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×Product details
- Date Published: November 2023
- format: Hardback
- isbn: 9781009299510
- length: 271 pages
- dimensions: 234 x 155 x 21 mm
- weight: 0.53kg
- availability: Available
Table of Contents
1. Introduction to privacy-preserving computing
2. Secret sharing
3. Homomorphic encryption
4. Oblivious transfer
5. Garbled circuit
6. Differential privacy
7. Trusted execution environment
8. Federated learning
9. Privacy-preserving computing platforms
10. Case studies of privacy-preserving computing
11. Future of privacy-preserving computing
References
Index.
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