Description: Privacy-Preserving Machine Learning by Sam Hamilton, Srinivasa Rao Aravilli Estimated delivery 3-12 business days Format Paperback Condition Brand New Description 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. 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. Details ISBN 1800564678 ISBN-13 9781800564671 Title Privacy-Preserving Machine Learning Author Sam Hamilton, Srinivasa Rao Aravilli Format Paperback Year 2024 Pages 402 Publisher Packt Publishing Limited GE_Item_ID:159977419; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. 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Price: 52.47 USD
Location: Fairfield, Ohio
End Time: 2024-11-25T04:31:24.000Z
Shipping Cost: 0 USD
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Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
ISBN-13: 9781800564671
Book Title: Privacy-Preserving Machine Learning
Number of Pages: 332 Pages
Publication Name: Privacy-Preserving Machine Learning : Use Cases Driven Approach to Develop and Protect Machine Learning Pipelines from Privacy and Security Threats
Language: English
Publisher: Packt Publishing, The Limited
Subject: Security / Online Safety & Privacy, General
Publication Year: 2024
Type: Textbook
Subject Area: Computers, Science
Item Length: 92.5 in
Author: Srinivasa Rao Aravilli
Item Width: 75 in
Format: Trade Paperback
