Cambridge: Cambridge University Press, 2023. - 268 p. - ISBN 1009299514.
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.
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