Introduction to data anonymisation: Techniques and case studies

Data anonymization defines the methodology and set of best practices and techniques that reduce the risk of identifying individuals, the irreversibility of the anonymization process, and the auditing of the exploitation of anonymized data by monitoring who, when, and for what purpose they are used. 

This process is essential, both when we talk about open data and general data, to protect people's privacy, guarantee regulatory compliance, and fundamental rights. 

The report "Introduction to Data Anonymization: Techniques and Practical Cases," prepared by Jose Barranquero, defines the key concepts of an anonymization process, including terms, methodological principles, types of risks, and existing techniques. 

The objective of the report is to provide a sufficient and concise introduction, mainly aimed at data publishers who need to ensure the privacy of their data. It is not intended to be a comprehensive guide but rather a first approach to understand the risks and available techniques, as well as the inherent complexity of any data anonymization process. 

What techniques are included in the report?  

After an introduction where the most relevant terms and basic anonymization principles are defined, the report focuses on discussing three general approaches to data anonymization, each of which is further integrated by various techniques: 

• Randomization: data treatment, eliminating correlation with the individual, through the addition of noise, permutation, or Differential Privacy.
• Generalization: alteration of scales or orders of magnitude through aggregation-based techniques such as K-Anonymity, L-Diversity, or T-Closeness.
• Pseudonymization: replacement of values with encrypted versions or tokens, usually through HASH algorithms, which prevent direct identification of the individual unless combined with additional data, which must be adequately safeguarded. 

The document describes each of these techniques, as well as the risks they entail, providing recommendations to avoid them. However, the final decision on which technique or set of techniques is most suitable depends on each particular case. 

The report concludes with a set of simple practical examples that demonstrate the application of K-Anonymity and pseudonymization techniques through encryption with key erasure. To simplify the execution of the case, users are provided with the code and data used in the exercise, available on GitHub. To follow the exercise, it is recommended to have minimal knowledge of the Python language. 

You can now download the complete report, as well as the executive summary and a summary presentation.