Privacy-Preserving Machine Learning: A use-case-driven approach to building and

Description: Privacy-Preserving Machine Learning by Sam Hamilton, Srinivasa Rao Aravilli This book helps software engineers, data scientists, ML and AI engineers, and research and development teams to learn and implement privacy-preserving machine learning as well as protect companies against privacy breaches. FORMAT Paperback CONDITION Brand New Publisher Description 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 breachesKey FeaturesUnderstand 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 attacksPurchase of the print or Kindle book includes a free PDF eBookBook 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, youll 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– Youll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field– Upon completion, youll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learnStudy 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 attacksWho 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 Author Biography Srinivasa Rao Aravilli boasts 27 years of extensive experience in technology, research, and leadership roles, spearheading innovation in various domains such as Information Retrieval, Search, ML/AI, Distributed Computing, Network Analytics, Privacy, and Security. Currently working as a Senior Director of Machine Learning Engineering at Capital One, Bangalore, he has a proven track record of driving new products from conception to outstanding customer success. Prior to his tenure at Capital One, Srinivasa held prominent leadership positions at Visa, Cisco, and Hewlett Packard, where he led product groups focused on data privacy, machine learning, and Generative AI. He holds a Masters Degree in Computer Applications from Andhra University, Visakhapatnam, India. Table of Contents Table of ContentsIntroduction to Data Privacy, Privacy threats and breachesMachine Learning Phases and privacy threats/attacks in each phaseOverview of Privacy Preserving Data Analysis and Introduction to Differential PrivacyDifferential Privacy Algorithms, Pros and ConsDeveloping Applications with Different Privacy using open source frameworksNeed for Federated Learning and implementing Federated Learning using open source frameworksFederated Learning benchmarks, startups and next opportunityHomomorphic Encryption and Secure Multiparty ComputationConfidential computing - what, why and current statePrivacy Preserving in Large Language Models Details ISBN1800564678 Publisher Packt Publishing Limited ISBN-13 9781800564671 Format Paperback Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2023-07-21 NZ Release Date 2023-07-21 DEWEY 005.8 Audience Professional & Vocational Year 2024 Publication Date 2024-05-24 UK Release Date 2024-05-24 Author Srinivasa Rao Aravilli Pages 402 Subtitle A use-case-driven approach to building and protecting ML pipelines from privacy and security threats We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:159982796;

Price: 81.32 AUD

Location: Melbourne

End Time: 2024-11-24T03:06:07.000Z

Shipping Cost: 22.82 AUD