Protecting Sensitive Data in Huge Datasets - Cloud Tools You Can Use

This course demonstrates cloud tools for finding personally identifiable information in massive datasets and reducing privacy risks before public release. It applies k-anonymity, l-diversity, masking, removal, and coarsening through practical examples.

Syllabus

Introduction
Running your own predictions
Privacy and open data
Chat bot example
Cloud DLP
Can you use it in your pipeline
At least 90 classifiers
Defining rules
Transform data
Mask data
Identify risk
Date shifting
Custom detection
Dictionaries
Tokenization
Encryption
What is BigQuery
What do you do with this data
Definitions
Mission measures
K anonymity
Example Mexico
Quasi Identifiers
Automate
Measuring key anonymity
Eldiversity
Anonymity
Kmap anonymity
Delta presence
Key anonymity
Best practices
Public datasets
Sharing data
Differential privacy
Understanding your data
Contact Felipe