{"product_id":"privacy-preserving-machine-learning-a-use-case-driven-approach-to-building-and-protecting-ml-pipelines-from-privacy-and-security-threats-paperback","title":"Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eSrinivasa Rao Aravilli\u003c\/b\u003e (Author), \u003cb\u003eSam Hamilton\u003c\/b\u003e (Foreword by)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eGain 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\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches\u003c\/p\u003e\u003cp\u003e- Develop and deploy privacy-preserving ML pipelines using open-source frameworks\u003c\/p\u003e\u003cp\u003e- Gain insights into confidential computing and its role in countering memory-based data attacks\u003c\/p\u003e\u003cp\u003e- Purchase of the print or Kindle book includes a free PDF eBook\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- In an era of evolving privacy regulations, compliance is mandatory for every enterprise\u003c\/p\u003e\u003cp\u003e- Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information\u003c\/p\u003e\u003cp\u003e- 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\u003c\/p\u003e\u003cp\u003e- As you progress, you'll be guided through developing anti-money laundering solutions using federated learning and differential privacy\u003c\/p\u003e\u003cp\u003e- Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models\u003c\/p\u003e\u003cp\u003e- You'll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field\u003c\/p\u003e\u003cp\u003e- 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\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Study data privacy, threats, and attacks across different machine learning phases\u003c\/p\u003e\u003cp\u003e- Explore Uber and Apple cases for applying differential privacy and enhancing data security\u003c\/p\u003e\u003cp\u003e- Discover IID and non-IID data sets as well as data categories\u003c\/p\u003e\u003cp\u003e- Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks\u003c\/p\u003e\u003cp\u003e- Understand secure multiparty computation with PSI for large data\u003c\/p\u003e\u003cp\u003e- Get up to speed with confidential computation and find out how it helps data in memory attacks\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers\u003c\/p\u003e\u003cp\u003e- Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn)\u003c\/p\u003e\u003cp\u003e- Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Introduction to Data Privacy, Privacy threats and breaches\u003c\/p\u003e\u003cp\u003e- Machine Learning Phases and privacy threats\/attacks in each phase\u003c\/p\u003e\u003cp\u003e- Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy\u003c\/p\u003e\u003cp\u003e- Differential Privacy Algorithms, Pros and Cons\u003c\/p\u003e\u003cp\u003e- Developing Applications with Different Privacy using open source frameworks\u003c\/p\u003e\u003cp\u003e- Need for Federated Learning and implementing Federated Learning using open source frameworks\u003c\/p\u003e\u003cp\u003e- Federated Learning benchmarks, startups and next opportunity\u003c\/p\u003e\u003cp\u003e- Homomorphic Encryption and Secure Multiparty Computation\u003c\/p\u003e\u003cp\u003e- Confidential computing - what, why and current state\u003c\/p\u003e\u003cp\u003e- Privacy Preserving in Large Language Models\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 402\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.82 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 24, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44291502899302,"sku":"9781800564671","price":77.37,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/qsFtaJsRyc9781800564671.webp?v=1766866893","url":"https:\/\/infinitylightwa.com\/products\/privacy-preserving-machine-learning-a-use-case-driven-approach-to-building-and-protecting-ml-pipelines-from-privacy-and-security-threats-paperback","provider":"Infinity Light","version":"1.0","type":"link"}