Privacy-Preserving Machine Learning
Published on 21. April 2023
Book
Paperback/Softback
300 pages
978-1-61729-804-2 (ISBN)
Description
Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You'll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning.
Complex privacy-enhancing technologies are demystified through real world use cases forfacial recognition, cloud data storage, and more. Alongside skills for technical implementation, you'll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you're done, you'll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.
Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.
Complex privacy-enhancing technologies are demystified through real world use cases forfacial recognition, cloud data storage, and more. Alongside skills for technical implementation, you'll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you're done, you'll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.
Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.
Reviews / Votes
"An interesting and well-structured book about an emerging discipline that will certainly keep growing in importance." Alain Couniot"Gives a deep and thorough introduction into preserving privacy while using personal data for machine learning and data mining." HaraldKuhn
"Makes for a great introduction to privacy-preserving techniques whichmake full use of machine learning." Aditya Kaushik
"An interesting book under a rising hot topic: privacy. I like the way using examples and figures to illustrate concepts." XiangboMao
"The only book, that I am aware of, which goes into depths of data privacy while making sure it doesn't get boring for readers." VishweshRavi Shrimali
"An interesting book under a rising hot topic: privacy." XiangboMao
"A great resource to understand privacy preserving ML." DhivyaSivasubramanian
"A great book to getting a deep theoretical overview of the landscape of privacy preserving approaches while also getting hands-on pragmatic experience."Stephen Oates
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 235 mm
Width: 189 mm
Thickness: 17 mm
Weight
576 gr
ISBN-13
978-1-61729-804-2 (9781617298042)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
J. Morris Chang | Di Zhuang | G. Dumindu Samaraweera
Privacy-Preserving Machine Learning
E-Book
05/2023
1st Edition
Simon + Schuster LLC
€49.44
Available for download
Persons
J. Morris Chang is a professor in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He received his PhDfrom North Carolina State University. Since 2012, his research projects on cybersecurity and machine learning have been funded by DARPA and agencies under DoD. He hasled a DARPA project under the Brandeis Program, focusing on privacy-preserving computation over the internet for three years.
Di Zhuang received his BSc degree in computer science and information security from Nankai University, Tianjin, China. He is currently a PhD candidate in the Department of Electrical Engineering of University of South Florida, Tampa, USA. Heconducted privacy-preserving machine learning research under the DARPA Brandeis Program from 2015 to 2018.
G. Dumindu Samaraweera received his BSc degree in computer systems and networking from Curtin University, Australia, and a MSc in enterprise application development degree from Sheffield Hallam University, UK. He is currently reading for his PhD in electrical engineering at University of South Florida, Tampa.
Di Zhuang received his BSc degree in computer science and information security from Nankai University, Tianjin, China. He is currently a PhD candidate in the Department of Electrical Engineering of University of South Florida, Tampa, USA. Heconducted privacy-preserving machine learning research under the DARPA Brandeis Program from 2015 to 2018.
G. Dumindu Samaraweera received his BSc degree in computer systems and networking from Curtin University, Australia, and a MSc in enterprise application development degree from Sheffield Hallam University, UK. He is currently reading for his PhD in electrical engineering at University of South Florida, Tampa.