Название: Privacy-Preserving Machine Learning Автор: Издательство: Год: 2022 MEAP V8 Формат: True PDF Страниц: 323 Размер: 13,2 Mb Язык: English
Complex privacy-enhancing technologies are demystified through real-world use cases for facial recognition, cloud data storage, and more. 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 for facial 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.
What Is Federated Learning? Название: What Is Federated Learning? Автор: Emily Glanz, Nova Fallen Издательство: O’Reilly Media, Inc. Год: 2021-10-14 Язык: английский Формат:...
Differential Privacy and Applications Название: Differential Privacy and Applications Автор: Tianqing Zhu and Gang Li Издательство: Springer Год: 2017 Формат: PDF Размер: 10 Мб Язык:...