Loved by learners at thousands of companies
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.
Introduction to Data PrivacyFree
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.
Introduction to Differential Privacy
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.Differential privacy50 xpZero Privacy Budget50 xpChanging the Privacy Budget50 xpLess Privacy Protection50 xpLess Information Leakage50 xpGlobal sensitivity50 xpSensitivity of Counting and Proportion Queries100 xpSensitivity of Mean and Variance Queries100 xpLaplace mechanism50 xpCounting Query100 xpProportion Query100 xpMean and Variance Queries100 xp
Differentially Private Properties
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.Sequential composition50 xpValue of Epsilon per the Sequential Composition100 xpSequential Composition and Laplace mechanism100 xpParallel composition50 xpValue of Epsilon per the Parallel Composition100 xpParallel Composition and Laplace mechanism100 xpPost-processing50 xpSetting Epsilon and Global Sensitivity100 xpPost-processing and Laplace mechanism100 xpImpossible and inconsistent answers50 xpInconsistent answers100 xpNormalizing noise100 xp
Differentially Private Data Synthesis
In this chapter, you will learn how to release simple data sets publicly using differentially private data synthesis techniques.Laplace sanitizer50 xpPrepping for the Laplace sanitizer100 xpApplying the Laplace sanitizer100 xpNormalizing Noise and Generating Synthetic Data100 xpDifferential privacy (DP) parametric approaches50 xpPrepping for the DP Binomial Distribution100 xpGenerating Binomial Synthetic Data100 xpPrepping for the DP Normal Distribution100 xpGenerating Normal Synthetic Data100 xpWrap-up50 xp
In the following tracksStatistician
Postdoctoral Researcher at the Los Alamos National Laboratory
Claire McKay Bowen is a Postdoctoral Researcher in the Statistical Science Group at the Los Alamos National Laboratory. She conducts research in uncertainty quantification with physics-informed Bayesian Model updating and data privacy via differentially private data synthesis methods. Her other interests include statistical computing, scientific communication, and STEM outreach.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA