Privacy-preserving computing: for big data analytics and AI

Table of contents:
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.

[https://www.cambridge.org/core/books/privacypreserving-computing/87950F31D77A7E0A5745607399471D99#fndtn-information]

Select 1 - Introduction to Privacy-preserving Computing
1 - Introduction to Privacy-preserving Computingpp 1-12
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Select 2 - Secret Sharing
2 - Secret Sharingpp 13-35
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Select 3 - Homomorphic Encryption
3 - Homomorphic Encryptionpp 36-62
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Select 4 - Oblivious Transfer
4 - Oblivious Transferpp 63-68
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Select 5 - Oblivious Transfer
5 - Oblivious Transferpp 69-79
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Select 6 - Differential Privacy
6 - Differential Privacypp 80-104
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Select 7 - Trusted Execution Environment
7 - Trusted Execution Environmentpp 105-120
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Select 8 - Federated Learning
8 - Federated Learningpp 121-149
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Select 9 - Privacy-preserving Computing Platforms
9 - Privacy-preserving Computing Platformspp 150-193
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Select 10 - Case Studies of Privacy-preserving Computing
10 - Case Studies of Privacy-preserving Computingpp 194-232
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Select 11 - Future of Privacy-preserving Computing
11 - Future of Privacy-preserving Computingpp 233-237


(https://www.cambridge.org/core/books/privacypreserving-computing/87950F31D77A7E0A5745607399471D99)