Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning - Nyomtatható verzió

+- HHWForum.hu (https://hhwforum.hu)
+-- Fórum: Letöltések (https://hhwforum.hu/forumdisplay.php?fid=9)
+--- Fórum: E-könyvek (https://hhwforum.hu/forumdisplay.php?fid=57)
+---- Fórum: Külföldi könyvek (https://hhwforum.hu/forumdisplay.php?fid=64)
+---- Téma: Privacy-Preserving Machine Learning (/showthread.php?tid=181216)

RE: Privacy-Preserving Machine Learning - book24h - 2024-11-20

[Kép: df8fb9f302636ca41b5bd01416c1db4b.webp]

Free Download Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats by Srinivasa Rao Aravilli
English | May 24, 2024 | ISBN: 1800564678 | True EPUB/PDF | 402 pages | 10.3/31.2 MB
Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches

Key Features
Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breachesDevelop and deploy privacy-preserving ML pipelines using open-source frameworksGain insights into confidential computing and its role in countering memory-based data attacks
Book Description

In an era of evolving privacy regulations, compliance is mandatory for every enterprise
Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information
This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases
As you progress, you'll be guided through developing anti-money laundering solutions using federated learning and differential privacy
Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models
You'll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field
Upon completion, you'll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks

What you will learn
Study data privacy, threats, and attacks across different machine learning phasesExplore Uber and Apple cases for applying differential privacy and enhancing data securityDiscover IID and non-IID data sets as well as data categoriesUse open-source tools for federated learning (FL) and explore FL algorithms and benchmarksUnderstand secure multiparty computation with PSI for large dataGet up to speed with confidential computation and find out how it helps data in memory attacks
Who this book is for

This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers
Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn)
Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques

Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.

Links are Interchangeable - Single Extraction