Technical Glossary
What is anonymization for visual/physical data?
It’s the process of transforming images, video, and 3D/sensor captures so people, places, or assets can’t be linked back to an identifiable subject while keeping the data useful for training and testing computer vision. Think “privacy-first visuals that still work for ML.”
What is anonymization for visual/physical data?
It’s the process of transforming images, video, and 3D/sensor captures so people, places, or assets can’t be linked back to an identifiable subject while keeping the data useful for training and testing computer vision. Think “privacy-first visuals that still work for ML.”
How is it different from tabular anonymization?
How is it different from tabular anonymization?
What counts as a direct identifier in visuals?
What counts as a direct identifier in visuals?
And indirect (quasi-) identifiers?
And indirect (quasi-) identifiers?
What is “synthetic replacement”?
What is “synthetic replacement”?
Core techniques for visual anonymization
Core techniques for visual anonymization
Where does “Physical AI” fit?
Where does “Physical AI” fit?
Is anonymized visual data still “personal data” under privacy laws?
Is anonymized visual data still “personal data” under privacy laws?
Anonymization vs. masking vs. pseudonymization (for visuals)
Anonymization vs. masking vs. pseudonymization (for visuals)
How do we measure privacy for images and video?
How do we measure privacy for images and video?
How do we ensure utility doesn’t drop?
How do we ensure utility doesn’t drop?
What about datasets with 3D or multi-sensor data (LiDAR, depth, stereo)?
What about datasets with 3D or multi-sensor data (LiDAR, depth, stereo)?
Common pitfalls
Common pitfalls
When should I use fully synthetic instead of anonymizing real visuals?
When should I use fully synthetic instead of anonymizing real visuals?
What documentation should accompany an anonymized visual dataset?
What documentation should accompany an anonymized visual dataset?
Typical Anonymization Pipeline (Visual-Only)
Typical Anonymization Pipeline (Visual-Only)
© 2025 - All rights reserved
Generate artificial, synthetic datasets with the same characteristics as real data, so you can improve AI models without compromising on privacy.
© 2025 - All rights reserved
Generate artificial, synthetic datasets with the same characteristics as real data, so you can improve AI models without compromising on privacy.
© 2025 - All rights reserved
Generate artificial, synthetic datasets with the same characteristics as real data, so you can improve AI models without compromising on privacy.