In the first part of this series, we discussed the importance, ethical considerations, and challenges of data anonymization. Now, let’s dive into various data anonymization techniques, their strengths, weaknesses, and their implementation in Python.
1. Data Masking
Data masking, or obfuscation involves hiding original data with random characters or data. This technique protects sensitive information like credit card numbers or personal identifiers in environments where data integrity is not critical. However, confidentiality is essential, such as in development and testing environments. For instance, a developer working on a banking application can use masked account numbers to test the software without accessing real account information. This method ensures that sensitive data remains inaccessible while the overall structure and format are preserved for practical use.
