Indian Institute of Management Udaipur Library

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Practical data privacy : enhancing privacy and security in data / Katharine Jarmul.

By: Jarmul, Katharine [author.].
Material type: materialTypeLabelBookPublisher: Sebastopol, CA : O'Reilly Media, 2023.Edition: First edition.Description: xxviii, 315 pages : illustrations ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 1098129466; 9781098129460.Subject(s): Data protection | Computer software -- Security measures | Privacy, Right of | Protection de l'information (Informatique) | Logiciels -- Sécurité -- Mesures | Computer software -- Security measures | Data protection | Privacy, Right ofDDC classification: 005.8
Contents:
Data governance and simple privacy approaches -- Anonymization -- Building privacy into data pipelines -- Privacy attacks -- Privacy-aware machine learning and data science -- Federated learning and data science -- Encrypted computation -- Navigating the legal side of privacy -- Privacy and practicality considerations -- Frequently asked questions (and their answers!) -- Go forth and engineer privacy!
Summary: Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.
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Item type Current location Call number Copy number Status Date due
Monograph Monograph Indian Institute of Management Udaipur
A2/3 		005.8 JK (Browse shelf) 1 Available

Includes index.

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.

Data governance and simple privacy approaches -- Anonymization -- Building privacy into data pipelines -- Privacy attacks -- Privacy-aware machine learning and data science -- Federated learning and data science -- Encrypted computation -- Navigating the legal side of privacy -- Privacy and practicality considerations -- Frequently asked questions (and their answers!) -- Go forth and engineer privacy!

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