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.
This chapter covers some basic data privacy techniques that statisticians use to anonymize data. You'll first learn how to remove identifiers and then generate synthetic data from probability distributions.
After covering the basic data privacy techniques, you'll learn conceptually about differential privacy as well as how to implement the most popular and common differentially private algorithm called the Laplace mechanism.
In this chapter, you will learn the various properties of differential privacy, such as the combination rules and post-processing, to properly implement the Laplace mechanism for various kinds data questions.
In this chapter, you will learn how to release simple data sets publicly using differentially private data synthesis techniques.
In the following tracksStatistician
Postdoctoral Researcher at the Los Alamos National Laboratory
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Lloyds Banking Group
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Harvard Business School
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Decision Science Analytics, USAA