Data Science meets Data Protection: Keeping your data secure while learning from it.

We will discuss anonymization and pseudonymization techniques that you can apply to your data to keep it secure and comply with the law(s) while still being able to gain useful insights from it.

• Why protect data?
• Pseudonymization vs. anonymization: What's the difference?
• Pseudonymization: Techniques & real-world examples
• Problems and risks when pseudonymizing data
• Anonymization: Approaches & real-world examples
• Problems and risks when anyonymizing data
• Takeaways and summary

We will show concrete Python implementations of various techniques and use example data sets to show how applying pseudonymization and anonymization will affect our ability to do machine learning / data science.