{"product_id":"privacy-preserving-data-science-with-r-differential-privacy-data-anonymization-synthetic-data-and-secure-machine-learning-for-real-world-data-prot-9798253552711","title":"Privacy Preserving Data Science with R: Differential Privacy, Data Anonymization, Synthetic Data, and Secure Machine Learning for Real-World Data Prot","description":"\u003cp\u003e • Author(s): Lamina J. a.\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePrivacy-Preserving Data Science with R: Differential Privacy, Data Anonymization, Synthetic Data, and Secure Machine Learning for Real-World Data Protection\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eNo GPS? No problem. With this book, you'll always know what's ahead.\u003c\/p\u003e\u003cp\u003eMost data science systems are built to extract insight not to protect people. That's why they fail under real scrutiny. Data gets re-identified. Models leak information. Dashboards expose patterns that should never be visible. And by the time it's discovered, the damage is already done.\u003c\/p\u003e\u003cp\u003eThis book takes a different approach.\u003c\/p\u003e\u003cp\u003eIt shows you how to design data systems that \u003cb\u003ework in the real world \u003c\/b\u003ewhere privacy is not optional, attackers are not hypothetical, and compliance is enforced.\u003c\/p\u003e\u003cp\u003eInstead of theory, you get \u003cb\u003epractical, production-ready workflows in R\u003c\/b\u003e for building systems that protect sensitive data without destroying analytical value.\u003c\/p\u003e\u003cp\u003eInside this book, you will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIdentify and eliminate re-identification risks before modeling begins\u003c\/li\u003e\n\u003cli\u003eBuild anonymization pipelines that hold up against real attacks\u003c\/li\u003e\n\u003cli\u003eApply differential privacy with controlled privacy budgets\u003c\/li\u003e\n\u003cli\u003eGenerate synthetic data without leaking original records\u003c\/li\u003e\n\u003cli\u003eSecure machine learning models against inference attacks\u003c\/li\u003e\n\u003cli\u003eReplace raw data access with safe query systems and APIs\u003c\/li\u003e\n\u003cli\u003eDesign privacy-first data pipelines from ingestion to deployment\u003c\/li\u003e\n\u003cli\u003eMeasure privacy and utility using defensible, audit-ready metrics\u003c\/li\u003e\n\u003cli\u003eBuild GDPR-ready and HIPAA-aware data systems\u003c\/li\u003e\n\u003cli\u003eSimulate real-world attacks and harden your systems against them\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis is not a theoretical guide. It is a \u003cb\u003ehands-on blueprint\u003c\/b\u003e for engineers, data scientists, and analysts who need to \u003cb\u003euse sensitive data without exposing it\u003c\/b\u003e.\u003c\/p\u003e\u003cp\u003eUnlike most books in this space, this guide focuses on: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eReal-world implementation in R not abstract frameworks\u003c\/li\u003e\n\u003cli\u003eEnd-to-end system design, not isolated techniques\u003c\/li\u003e\n\u003cli\u003eAttack simulation and defense, not just prevention\u003c\/li\u003e\n\u003cli\u003eMeasurable privacy, not vague compliance claims\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eIf you're working with customer data, healthcare data, financial data, or any system where privacy matters, this book gives you the tools to build systems that are \u003cb\u003esecure, scalable, and defensible\u003c\/b\u003e.\u003c\/p\u003e\u003cp\u003eBecause in modern data science, it's not enough to build models that work.\u003c\/p\u003e\u003cp\u003eYou have to build systems that \u003cb\u003esurvive scrutiny\u003c\/b\u003e.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47775699566743,"sku":"9798253552711","price":1728.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798253552711.webp?v=1777991939","url":"https:\/\/atlanticbooks.com\/products\/privacy-preserving-data-science-with-r-differential-privacy-data-anonymization-synthetic-data-and-secure-machine-learning-for-real-world-data-prot-9798253552711","provider":"Atlantic Books","version":"1.0","type":"link"}