R Tutorial: Intro to Anonymization (I)

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Hi. I am Claire Bowen, and I'm excited to teach you how to use R to apply basic anonymization techniques so you can publicly release data sets. You will also learn about a powerful mathematical principle called differential privacy. With social media and data being generated in large volumes everywhere, data privacy has been a growing public concern. Entities such as the US Census Bureau are promoting and implementing better privacy techniques to address this concern.

There are several different meanings of data privacy. As mentioned earlier, you will be focusing on ensuring the privacy of data sets released to the public, for example, healthcare data. In the United States, tax payer money is used to collect healthcare information, therefore, as a tax payer, you and the general public should have access to this data. Additionally, this type of data is extremely useful for medical researchers to discover potential contributors of cancer or improve personalized medicine. However, no one, not even those medical researchers, should know specifically who in the data set has cancer. Furthermore, the government should not deny access to healthcare data or other data sets that could benefit society.

In Chapter 1, you will learn basic anonymization techniques, such as removing identifiers and generating synthetic data.

In Chapter 2, you'll learn the concept of differential privacy and an algorithm that satisfies differential privacy, called the Laplace mechanism.

In Chapter 3, we'll cover various properties of differential privacy, such as the combination rules and post-processing.

Finally, in Chapter 4, you will learn how to release simple data sets publicly using differentially private data synthesis techniques.

You'll practice applying the various data privacy methods on two data sets. The first is a White House Staff Salary data set from Socrata that contains information for individual staff members about their employment details such as salaries. The second is a male fertility data set from the UCI Machine Learning Repository that was collected in order to determine infertility contributors, such as level of physical activity of a person and how smoking affects the health of sperm.

Before you start applying any privacy methods, you should always preview the data. Let’s take a quick look at our first data set called whitehouse. We see that there are a couple of variables containing personal information that we should change; Employee Names and Salary. Both are considered private information since most people would not want their names linked to a specific income. Under the Salary column, we see that the salaries are reported to the closest dollar, so we might want to change the salaries to be less specific. Note that this course will make extensive use of the dplyr package to manipulate data. Let's get started!

One basic method for anonymizing data is removing identifiers; or, in the case of our data set, removing the names. We can accomplish this by replacing the names with numbers. We saw from the tibble output that there are a total of 469 observations, so we will replace the names with the sequence of numbers from 1 to 469.

Another basic method is rounding continuous values. Based on the salaries we saw in the data set, we could round the salaries to the nearest hundred or thousand. Let's round to the nearest thousand by using the round() function and setting digits to -3.

With these methods in mind, let's try some examples.

#R #RTutorial #DataCamp #Anonymization #DataPrivacy