Artículo: AMZ-B0CLGM2L5G

Privacy-preserving Computing: for Big Data Analytics and AI

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Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.

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Highlight, take notes, and search in the book In this edition, page numbers are just like the physical edition

Privacy-preserving Computing: for Big Data Analytics and AI

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Regulating Digital Industries

Balancing Privacy and Free Speech (Routledge Research in Information Technology and E-Commerce Law)

Privacy-preserving Computing: for Big Data Analytics and AI

Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Privacy-Preserving Machine Learning

Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI

AI Governance: Secure, privacy-preserving, ethical systems

AltumView Sentinare Fall Detection & Activity Sensor, Privacy-Preserving Videoless Stick Figure View, Action Statistics, Medical Alert

Practical Data Privacy: Enhancing Privacy and Security in Data

Enabling Secure and Privacy Preserving Communications in Smart Grids (SpringerBriefs in Computer Science)

Building Production LLM Systems: The Practitioner's Guide to Open-Source AI: Design, Deploy, and Fine-Tune LLaMA, Mistral, and Self-Hosted Language Models for Scalable, Privacy-Preserving Enterprise

Protecting Your Privacy in a Data-Driven World (ASA-CRC Series on Statistical Reasoning in Science and Society)

Synthetic-Data Engineering Privacy-First Data Lakes for ML Without Real Users

Privacy Enhancing Techniques: Practices and Applications

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Privacy-preserving Computing: for Big Data Analytics and AI

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Productos Relacionados

Regulating Digital Industries

Balancing Privacy and Free Speech (Routledge Research in Information Technology and E-Commerce Law)

Otros Productos

Privacy-preserving Computing: for Big Data Analytics and AI

Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Privacy-Preserving Machine Learning

Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI

AI Governance: Secure, privacy-preserving, ethical systems

AltumView Sentinare Fall Detection & Activity Sensor, Privacy-Preserving Videoless Stick Figure View, Action Statistics, Medical Alert

Practical Data Privacy: Enhancing Privacy and Security in Data

Enabling Secure and Privacy Preserving Communications in Smart Grids (SpringerBriefs in Computer Science)

Building Production LLM Systems: The Practitioner's Guide to Open-Source AI: Design, Deploy, and Fine-Tune LLaMA, Mistral, and Self-Hosted Language Models for Scalable, Privacy-Preserving Enterprise

Protecting Your Privacy in a Data-Driven World (ASA-CRC Series on Statistical Reasoning in Science and Society)

Synthetic-Data Engineering Privacy-First Data Lakes for ML Without Real Users

Privacy Enhancing Techniques: Practices and Applications

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