Responsible Data Guidelines – Additional resources

Data Privacy and Anonymization in R

By DataCamp |

With social media and big data everywhere, data privacy has been a growing, public concern. Recognizing this issue, entities are promoting better privacy techniques; specifically differential privacy, a mathematical condition that quantifies privacy risk. In this course, you will learn to code basic data privacy methods and a differentially private algorithm based on various differentially private properties. With these tools in hand, you will learn how to generate a basic synthetic (fake) data set with the differential privacy guarantee for public data release.

Topic(s): Anonymization, pseudonymization and de-identification, Privacy & confidentiality
Type of resource: Training, courses and certification

This document is a suggested additional resource in our Responsible Data Guidelines.