{"product_id":"privacy-preserving-machine-learning-srinivasa-rao-aravilli-9781800564671","title":"Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats","description":"\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\u003eKey Features\u003cul\u003e\n\u003cli\u003eUnderstand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches\u003c\/li\u003e\n\u003cli\u003eDevelop and deploy privacy-preserving ML pipelines using open-source frameworks\u003c\/li\u003e\n\u003cli\u003eGain insights into confidential computing and its role in countering memory-based data attacks\u003c\/li\u003e\n\u003cli\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003ePrivacy regulations are evolving each year and compliance with privacy regulations is mandatory for every enterprise. Machine learning engineers are required to not only analyze large amounts of data to gain crucial insights, but also comply with privacy regulations to protect sensitive data. This may seem quite challenging considering the large volume of data involved and lack of in-depth expertise in privacy-preserving machine learning.\u003c\/p\u003e\u003cp\u003eThis book delves into data privacy, machine learning privacy threats, and real-world cases of privacy-preserving machine learning, as well as open-source frameworks for implementation. You'll be guided through developing anti-money laundering solutions via federated learning and differential privacy. Dedicated sections also address data in-memory attacks and strategies for safeguarding data and ML models. The book concludes by discussing the necessity of confidential computation, privacy-preserving machine learning benchmarks, and cutting-edge research.\u003c\/p\u003e\u003cp\u003eBy the end of this machine learning book, you'll be well-versed in privacy-preserving machine learning and know how to effectively protect data from threats and attacks in the real world.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eStudy data privacy, threats, and attacks across different machine learning phases\u003c\/li\u003e\n\u003cli\u003eExplore Uber and Apple cases for applying differential privacy and enhancing data security\u003c\/li\u003e\n\u003cli\u003eDiscover IID and non-IID data sets as well as data categories\u003c\/li\u003e\n\u003cli\u003eUse open-source tools for federated learning (FL) and explore FL algorithms and benchmarks\u003c\/li\u003e\n\u003cli\u003eUnderstand secure multiparty computation with PSI for large data\u003c\/li\u003e\n\u003cli\u003eGet up to speed with confidential computation and find out how it helps data in memory attacks\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis book is for data scientists, machine learning engineers, and privacy engineers who have working knowledge of mathematics as well as basic knowledge in any one of the ML frameworks (TensorFlow, PyTorch, or scikit-learn).\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eIntroduction to Data Privacy, Privacy threats and breaches\u003c\/li\u003e\n\u003cli\u003eMachine Learning Phases and privacy threats\/attacks in each phase\u003c\/li\u003e\n\u003cli\u003eOverview of Privacy Preserving Data Analysis and Introduction to Differential Privacy\u003c\/li\u003e\n\u003cli\u003eDifferential Privacy Algorithms, Pros and Cons\u003c\/li\u003e\n\u003cli\u003eDeveloping Applications with Different Privacy using open source frameworks\u003c\/li\u003e\n\u003cli\u003eNeed for Federated Learning and implementing Federated Learning using open source frameworks\u003c\/li\u003e\n\u003cli\u003eFederated Learning benchmarks, startups and next opportunity\u003c\/li\u003e\n\u003cli\u003eHomomorphic Encryption and Secure Multiparty Computation\u003c\/li\u003e\n\u003cli\u003eConfidential computing - what, why and current state\u003c\/li\u003e\n\u003cli\u003ePrivacy Preserving in Large Language Models\u003c\/li\u003e\n\u003c\/ol\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Srinivasa Rao Aravilli\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 1800564678\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9781800564671\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Packt Publishing\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 05\/24\/2024\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 402\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.52lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.25h x 7.50w x 0.82d","brand":"Srinivasa Rao Aravilli","offers":[{"title":"Paperback","offer_id":45934165688575,"sku":"9781800564671","price":44.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_e2b83dbc-9e2a-4db0-8ca7-bd5b90925bf3.jpg?v=1721790501","url":"https:\/\/www.whiterainbookhouse.com\/products\/privacy-preserving-machine-learning-srinivasa-rao-aravilli-9781800564671","provider":"WR Book House","version":"1.0","type":"link"}