Hello, fellow data enthusiasts! In this post, we will delve into the details of data anonymization and its significance. If you’re interested in learning more about the differences between anonymization and pseudonymization, check out my related article on the topic. In this post, I will cover the different types of data anonymization techniques that are commonly used and provide code examples for each technique using Python. I will also explore several real-world applications of data anonymization across various industries.
The codes in this post are available here.
Contents
Introduction
Data anonymization is the process of protecting private or sensitive information by removing or encrypting identifiers that connect an individual to stored data. This includes personally identifiable information (PII), protected health information (PHI), and other data that can be used by third parties to identify a person. Data anonymization aims to preserve data subjects’ privacy and confidentiality, while maintaining the integrity and usability of the data.
