Abstract
In an era where data privacy is paramount, this workshop offers a comprehensive exploration of data masking, pseudonymization, and anonymization techniques. You will gain hands-on experience with Python-based tools, learning to protect sensitive information while maintaining data utility.
We will delve into how Machine Learning and Artificial Intelligence can enhance these processes, ensuring robust data protection. Additionally, the workshop will cover applications in regulatory compliance, secure data sharing, and synthetic data generation for testing and development.
By the end, you will be equipped to implement effective data protection strategies, addressing both privacy concerns and practical data usage needs.
Objectives
– Understand data masking, pseudonymization, and anonymization techniques
– Apply Python-based tools for data protection
– Integrate ML and AI in data privacy processes
– Generate synthetic datasets for testing and development
Target Group
Data scientists, ML practitioners, data protection officers, and executives responsible for data privacy (a balanced approach to theoretical concepts and practical applications caters to both technical and non-technical audiences)
Prerequisites
Laptop, basic knowledge of Python is beneficial for the hands-on exercises but not mandatory