Data Privacy and Anonymization in R

Publicly release data sets with a differential privacy guarantee.

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4 Hours13 Videos45 Exercises3,426 Learners
3650 XP

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Course Description

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.

  1. 1

    Introduction to Data Privacy

    Free

    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.

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    Intro to anonymization (I)
    50 xp
    Removing Names
    100 xp
    Rounding Salaries
    100 xp
    Intro to anonymization (II)
    50 xp
    Generalization
    100 xp
    Bottom Coding
    100 xp
    summarize_at()
    100 xp
    count()
    100 xp
    Data synthesis
    50 xp
    Binomial Distribution
    100 xp
    Normal Distribution
    100 xp
  2. 2

    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.

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In the following tracks

Statistician

Datasets

Data sets

Collaborators

Sumedh PanchadharChester Ismay
Claire Bowen Headshot

Claire Bowen

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.
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