Privacy-preserving computing for big data analytics and AI
- Cambridge : Cambridge University Press, 2022
- xii, 255 p. ; ill., 24 cm.
Includes bibliographical references and index.
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.
9781009299510 hbk.
Big data USENIX Privacy-preserving computing Secure multiparty computation Trusted Execution Environment Oblivious Transfer Bloom filter Ciphertext Data privacy Differential privacy Homomorphic encryption Computer security Big data Information technology Security measures privacy Right of IT security Security Intel SGX Machine learning