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