R Tutorial: Intro to Anonymization (I)

Key Takeaways

hi I'm Clara Bowen and I'm excited to teach you how to use our to apply basic anonymization techniques so you can publicly release datasets 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'll be focusing on ensuring the privacy of datasets released to the public for example health care data in the United States taxpayer money is used to collect health care information therefore as a taxpayer 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 dataset has cancer furthermore the government should not deny access to health care data or other datasets that could benefit Society in Chapter one you will learn basic anonymization techniques such as removing identifiers and generating synthetic data in Chapter two you'll learn the concept of differential privacy and an algorithm that satisfies differential privacy called the Laplace mechanism in Chapter three we'll cover various properties of differential privacy such as the combination rules and post-processing finally in Chapter four you will learn how to release simple datasets publicly using differentially private data synthesis techniques you'll practice applying the various data privacy methods on two datasets the first is a white house staff salary data set from secreta that contains information for individual staff members about their employment details such as Valerie's 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 white house we see 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 deep liar 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 table output that are a total of 469 observations so we will replace the names with a 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 100 or thousand let's round to the nearest thousand by using the round function and setting digits 2-3 with these methods in mind let's try some examples