Practical Data Privacy
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Practical Data Privacy
Author: Katharine Jarmul
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-04-19
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. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?
Practical Data Privacy
Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure for data scientists to ensure data privacy. Unfortunately, integrating privacy into your data science workflow is still complicated. This essential guide will give you solid advice and best practices on breakthrough privacy-enhancing technologies such as encrypted learning and differential privacy--as well as a look at emerging technologies and techniques in the field. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my project? What does "anonymized data" really mean? Should I anonymize the data? If so, how? Which privacy techniques fit my project and how do I incorporate them? What are the differences and similarities between privacy-preserving technologies and methods? How do I utilize an open-source library for a privacy-enhancing technique? How do I ensure that my projects are secure by default and private by design? How do I create a plan for internal policies or a specific data project that incorporates privacy and security from the start?
99 Privacy Breaches to Beware Of: Practical Data Protection Tips from Real Life Experiences
Author: Kevin Shepherdson
language: en
Publisher: Marshall Cavendish International Asia Pte Ltd
Release Date: 2018-08-15
Data protection laws are new in Singapore, Malaysia, Philippines, Indonesia and Thailand. In Europe, the General Data Protection Regulation (GDPR) — a single law across all of EU – comes into force from May 2018. There are also strict laws in the US that govern the processing of personal data. Over a hundred countries in the world have a comprehensive data protection law and it is very easy for individuals and companies to breach these laws. Data or privacy breaches are on the rise and businesses can be prosecuted under data protection laws. Fines for non-compliance can be from S$1 million in Singapore, up to three years jail in Malaysia, and up to 4% of global revenues for EU countries. The focus on this book is operational compliance. The book is for everyone as all of us in the course of our daily work process personal data. Organised into sections, each idea provides practical advice and examples of how a breach of the law may happen. Examples cover HR, Finance, Admin, Marketing, etc, allowing the reader to relate to his or her own area of work