Welcome to the Data Anonymization Course!

This website is a work in progress. Please come back later in 2026 :)

Tutorial Overview

This self-paced tutorial on anonymization of quantitative research data is intended to take about three hours to complete.

The tutorial is split into the following sections:

  1. FOUNDATIONS OF DATA PROTECTION talks about data protection basics in ethics and law, mechanisms of data protection in research, and basic terms.
  2. DATA ANONYMIZATION PROCESS walks you through the process of anonymizing your research data based on example data.
  3. BALANCING DATA PROTECTION AND OPENNESS presents methods for aligning your data protection and open science interests.
  4. ANONYMIZATION WORKFLOW closes this tutorial by summarizing the learned workflow.

What You’ll Learn

By the end of this tutorial, you will be able to:

  • Understand key concepts in the world of privacy (e.g., anonymization, k-anonymity)
  • Classify data in relevant categories for data protection (e.g., personal data, sensitive data)
  • Apply anonymization techniques using R in a coherent workflow
  • Make informed decisions when balancing the risks and utility of the anonymized data

What You Will NOT Learn

You will not learn anything other than anonymization of quantitative tabular data.

Here are a few helpful links for other data types:

Prerequisites

  • You need basic R skills (e.g., loading data and packages). Experience with data wrangling with tidyverse is beneficial.

Required Software

To follow along with the hands-on exercises, you will need a recent version of R (version 4.1.0 or newer) and RStudio.

You will also need a handful of R packages. Install them with:

install.packages("sdcMicro")  # measure re-identification risk and apply anonymization techniques
install.packages("tidyverse") # data wrangling and plotting, used throughout the exercises
install.packages("here")      # locate your data file regardless of the working directory
install.packages("knitr")     # render tables in the tutorial's examples
install.packages("xfun")      # small helpers used in the tutorial's examples

The Example Dataset

Throughout the tutorial, we work with the same simulated dataset of 200 Germans. The data’s purpose is to answer whether certain political opinions are linked to religion. We will assess its privacy risks and then anonymize it step by step across the exercises. Download it here:

⬇️ Download SimulatedData.csv

Once downloaded, copy this into a new Markdown file in RStudio to import the data for further analysis:

# Load data based on downloaded file
data <- read.csv(here::here("SimulatedData.csv")) # Change based on the location of your data file

The dataset contains the following variables:

Variable Name Description Item Values
id Number assigned to each participant in order of participation (assigned in background) Integer; 1-200
name First and last name of participant Please indicate your full name (first and last name) String of characters
email Email address of participant What is your e-mail address? String of characters
plz German postal code What is your postal code? String of characters
gender Gender of participant What is your gender? Factor; “male”/“female”/“non-binary”
age Age of participant in years What is your age in years? Integer; 18-100
income Personal annual income in Euros What was your income over the last twelve months Integer
religion Religion of participant What is your religion? Factor; “Catholicism”,“Protestantism”,“Islam”,“Eastern Orthodoxy”,“Judaism”, “Buddhism”, “Hinduism”, “Other”, “None”
education Highest degree of education What is your highest degree of education? Factor; “no degree”,“trade school”,“high school”, “university”,“doctoral title”
pol_immigration Likert item measuring opinion on immigration The government should limit immigration more strictly than it currently does. Integer; 1-5
pol_environment Likert item measuring opinion on environment Protecting the environment should be a top priority, even if it slows economic growth. Integer; 1-5
pol_redistribution Likert item measuring opinion on redistribution of wealth The government should reduce income differences between rich and poor. Integer; 1-5
pol_eu_integration Likert item measuring opinion on membership in EU Our country benefits from being a member of the European Union. Integer; 1-5
ip_address IP address (version 4) of participant’s device when answering survey (collected in background) String of characters
years_in_job Number of years the participant has been in their current job How many years have you been in your current job? Integer; 0-n
Back to top