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Privacy-Preserving Machine Learning: secure user data without sacrificing model accuracy

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Privacy Preserving Machine Learning is a comprehensive introduction to data privacy in machine learning. Based on years of DARPA-funded cybersecurity research, the book is filled with lightbulb moments that will change the way you think about algorithm design. You’ll learn how to apply privacy-enhancing techniques to common machine learning tasks, and experiment with source code fresh from the latest academic papers.

Learn more about the book here: http://mng.bz/go5Z

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Privacy-Preserving Machine Learning: secure user data without sacrificing model accuracy

  1. 1. Protect sensitive data and retain ML model accuracy with Privacy-Preserving Machine Learning. Take 40% off by entering slzhuang into the discount code box at checkout at manning.com.
  2. 2. Keep sensitive user data safe and secure, without sacrificing the accuracy of your machine learning models. From search histories to medical records, many machine learning systems are trained on personal and sensitive data. It’s an ongoing challenge to keep the private details of users secure throughout the ML process without adversely affecting the performance of your models.
  3. 3. Privacy Preserving Machine Learning is a comprehensive introduction to data privacy in machine learning. Based on years of DARPA-funded cybersecurity research, the book is filled with lightbulb moments that will change the way you think about algorithm design. You’ll learn how to apply privacy-enhancing techniques to common machine learning tasks, and experiment with source code fresh from the latest academic papers.
  4. 4. This book 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. 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.
  5. 5. What people are saying about the book: An interesting book under a rising hot topic: privacy. I like the way using examples and figures to illustrate concepts. -Xiangbo Mao Gives a deep and thorough introduction into preserving privacy while using personal data for machine learning and data mining. -Harald Kuhn A great resource to understand privacy preserving ML. -Dhivya Sivasubramanian
  6. 6. About the authors: J. Morris Chang is a professor in the Department of Electrical Engineering of the University of South Florida, Tampa, USA. He received his PhD from North Carolina State University. Since 2012, his research projects on cybersecurity and machine learning have been funded by DARPA and agencies under DoD. He has led 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. He conducted 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.
  7. 7. Take 40% off Privacy-Preserving Machine Learning by entering slzhuang into the discount code box at checkout at manning.com. If you want to learn more about the book, check it out on our browser-based liveBook platform here.

Privacy Preserving Machine Learning is a comprehensive introduction to data privacy in machine learning. Based on years of DARPA-funded cybersecurity research, the book is filled with lightbulb moments that will change the way you think about algorithm design. You’ll learn how to apply privacy-enhancing techniques to common machine learning tasks, and experiment with source code fresh from the latest academic papers. Learn more about the book here: http://mng.bz/go5Z

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