CROZAnonyx

Pseudonymization replaces identifiable information within a dataset with pseudonyms, making it impossible to link the data back to an individual without additional information stored separately.

Data masking involves replacing sensitive data with fictional but realistic values while maintaining its original format and structure. This method ensures that real data is hidden but still functional for analysis or testing purposes.

Data swapping is a technique where values within a dataset are exchanged to preserve relationships between data points while anonymizing individual entries. This ensures that the overall structure and utility of the data remain intact.

Synthetic Data Generation creates artificial data that mimic the real one. It doesn’t rely on original records, reducing privacy risks to zero, and is especially useful for testing, training AI models, or sharing data across teams without exposing any real personal information.

Free-text Anonymization uses AI models to detect and remove sensitive information from unstructured text, like emails, support tickets, or medical notes. It can understand context, handle language nuances, and adapt to various data formats.