Simple data anonymization

Data anonymization in machine learning involves modifying personally identifiable information (PII) in datasets to prevent individual identification. This process is vital for maintaining data privacy and avoiding disclosure risks.

Different data formats including personal data, images, and videos require different anonymization techniques, balancing data utility with privacy preservation. The simplest way to achieve anonymization is by removing sensitive data not needed for the model or application or preprocessing the data to obscure them in a non-identifiable manner. This includes replacing it with a hash (hashing), using placeholders in a column (masking), or adding random noise to numerical data to obfuscate actual values (obfuscation). Here is an example using Pandas and Keras to show the use of this technique in a machine learning setup.

Let’s assume we have a dataset with user information, including sensitive attributes such as names and...