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.

‘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