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?
General
Imprint: |
O'Reilly Media
|
Country of origin: |
United States |
Release date: |
May 2023 |
Authors: |
Katharine Jarmul
|
Dimensions: |
233 x 178mm (L x W) |
Format: |
Paperback
|
Pages: |
300 |
ISBN-13: |
978-1-09-812946-0 |
Categories: |
Books >
Computing & IT >
General
Promotions
|
LSN: |
1-09-812946-6 |
Barcode: |
9781098129460 |
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