Qiang Yang

Kai Chen

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.

for Big Data Analytics and AI

Privacy-preserving Computing

This book explores how children have been affected by armed conflict in the borderlands of Thailand, particularly in the region abutting the Thailand-Myanmar border, and in the most southern part of Thailand. The author argues that the Thai government has made great efforts to protect children from armed conflict in these borderlands. The author analyzes the obstacles facing the Thai government in protecting children from armed conflict in the borderlands, and advances alternative solutions for how the Thai government might better protect children from armed conflict in the foreseeable future. This book not only opens a window for future research on children affected by armed conflict in the borderlands of Thailand and beyond, but also contributes to the breadth of perspective and depth of expertise in related fields, such as studies of human insecurity. It is relevant to scholars, graduate students, and policymakers interested in the impact of armed conflict on children. .

Kai Chen

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