This talk will explore the concept of automated text anonymization powered by machine learning, a solution that can quickly and accurately protect sensitive data. The talk will cover all the necessary concepts and considerations to implement such a solution in the industry. Some of the key concepts that will be discussed include the data labeling process, training NLP models, a human-in-the-loop process to ensure privacy and monitor the performance of the ML model, and implementation of MLOPS principles.

1. Anonymization 2.0
AI-Driven solution for text anonymization
Ivan Križanić

2. What is text anonymization?
Hello, my name is Ivan Krizanic, I’m working for Croz AI which
is located at Lastovska 23, Zagreb.

3. What is text anonymization?
Hello, my name is <anon>, I’m working for <anon> which is
located at <anon>.

4. Why anonymization?
Privacy matters

5. In the cloud

6. How?
AI of course!

7. Large Language
Models

8. Model Training and Fine Tuning
Open-source
datasets
Synthetic
datasets
Client data

9. Data Labelling
Precise instructions
„dr. Max Weber Clinic”
„Croz d.o.o Zagreb”
Label consolidation

10. Step 1 - Model

11. Rule Engine
Phone number
E-mail
URL
Static maps

12. Step 2 - PII Detection Module

14. Human in the Loop - AWS

15. Human in the Loop - Azure

17. Synthetic replacement
Hello, my name is <anon>, I’m working for <anon>
which is located at <anon>.
Hello, my name is [PER], I’m working for [ORG] which is
located at [LOC].

18. Synthetic replacement
Hello, my name is John Robinson, I’m working for Futura
Solution which is located at 80 Albert Road London.

19. Dataset Collection

20. Continuous Improvement

21. Downstream Application
Sentiment analysis
Topic modelling
Summarization
Semantic search

22. Key takeaways
ML workloads in cloud
Combination of static rules
and custom AI models
Human in the loop
Continuous monitoring and
improvement

23. Thank You
&
let’s connect