Publicly release data sets with a differential privacy guarantee.
With social media and big data everywhere, data privacy has been a growing, public concern. Recognizing this issue, entities such as Google, Apple, and the US Census Bureau 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.
With social media and big data everywhere, data privacy has been a growing, public concern. Recognizing this issue, entities such as Google, Apple, and the US Census Bureau 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.
