Data privacy is a key challenge in the development of artificial intelligence. As AI models increasingly rely on vast amounts of data, protecting sensitive information while maintaining usability is more critical than ever.
As part of the SIESTA research project, within the European Open Science Cloud (EOSC), Judith Sáinz-Pardo Díaz and Álvaro López García have been exploring solutions for handling sensitive data securely in AI applications. One such solution is Anjana, a tool designed to anonymize data while preserving its effectiveness for machine learning model
Why Is Data Anonymization Important?
AI applications require large datasets for training, but these datasets often contain personal or sensitive information. Without proper anonymization, organizations risk exposing private data, leading to compliance issues and ethical concerns. Anjana provides a way to transform sensitive data into a privacy-preserving format without losing its analytical value.
What You’ll Learn in This Tutorial
In our latest video, we demonstrate how Anjana works and how it can be used to anonymize data effectively. The tutorial covers:
🔒 How Anjana helps protect privacy in AI – Learn how anonymization techniques ensure data privacy while allowing for meaningful AI applications.
⚙️ Practical steps for data anonymization – A step-by-step guide to using Anjana to process and anonymize datasets.
📂 Key considerations for handling sensitive information – Best practices for ensuring compliance with data protection regulations and ethical AI development.