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.

Privacy-preserving Computing: for Big Data Analytics and AI

Computers & Technology

Yang, Qiang

Chen, Kai

Computer Science

Cambridge University Press

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Transfer Learning

Dai, Wenyuan

Pan, Sinno Jialin

张羽

The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)

Hutchinson, Ben

Raskar, Ramesh

Mohri, Mehryar

Liu, Yang

Yu, Han

Bennis, Mehdi

Evans,David

Song, Dawn

Joshi, Gauri

Kairouz, Peter

McMahan, H Brendan

Avent, Brendan

Bellet, Aurélien

Bhagoji, Arjun Nitin

Bonawit, Kallista

Charles, Zachary

Cormode, Graham

Cummings, Rachel

D'Oliveira, Rafael G L

Eichner, Hubert

El Rouayheb, Salim

Gardner, Josh

Garrett, Zachary

Gascón, Adrià

Ghazi, Badih

Gibbons, Phillip B

Gruteser, Marco

Harchaoui, Zaid

He, Chaoyang

He, Lie

Huo, Zhouyuan

Hsu, Justin

Jäggi, Martin

Javidi, Tara

Khodak, Mikhail

Konecný, Jakub

Korolova, Aleksandra

Koushanfar, Farinaz

Koyejo, Sanmi

Lepoint, Tancrède

Mittal, Prateek

Nock, Richard

Özgür, Ayfer

Pagh, Rasmus

Qi, Hang

Ramage, Daniel

Raykova, Mariana

Song, Weikang

Stich, Sebastian U

Sun, Ziteng

Theertha Suresh, Ananda

Tramèr, Florian

Vepakomma, Praneeth

Wang, Jianyu

Xiong, Li

Xu, Zheng

Yu, Felix X

Zhao, Sen

Now Publishers

Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private?
Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Cheng, Yong

Kang, Yan

Chen, Tianjian

Morgan Claypool

Morgan & Claypool Publishers

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Springer

Crafting Your Research Future: A Guide to Successful Master's and Ph.D. Degrees in Science & Engineering (Synthesis Lectures on Engineering, Science, and Technology)

Business & Money

Ling, Charles

Job Hunting & Careers

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