Explore techniques for protecting sensitive data in large datasets using cloud tools in this 46-minute conference talk. Learn to identify personally identifiable information (PII) in massive datasets, understand concepts like k-anonymity and l-diversity, and discover practical options for data protection such as removing, masking, and coarsening. Gain hands-on experience through real-life demonstrations on massive datasets, and discover newly available tools for PII detection. Delve into topics including Cloud DLP, BigQuery, tokenization, encryption, and differential privacy. Understand best practices for sharing public datasets while maintaining individual privacy, and learn how to automate anonymity measures. Acquire valuable insights on balancing data utility with protection of individuals when releasing public datasets.
Protecting Sensitive Data in Huge Datasets - Cloud Tools You Can Use
Overview
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
Taught by
NDC Conferences
