Data privacy e-book: Anonymization techniques

How to Enhance Privacy in Data Science

Cover

In the last two decades, the ability to collect personal information on individuals has opened up a new frontier, fueling innovation and enabling companies and organizations to deliver better, more personalized services at scale.

But innovation carries risks and this new frontier is rife with them, often calling for vast amounts of personal information to be digested into rich analytical products: reports, data sets, machine learning models.

This e-book examines methods for transforming data in a manner that protects the privacy of individuals while preserving utility.

Read on to learn about the following anonymization techniques:

  • De-identification
  • k-Anonymization
  • Differential Privacy
  • Local Differential Privacy
Vendor:
Immuta, Inc
Posted:
06 Aug 2019
Published:
06 Aug 2019
Format:
PDF
Length:
33 Page(s)
Type:
eBook
Language:
English
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