Protecting the privacy of patrons is of utmost importance for institutions that handle sensitive data, and in some cases, it's mandated by law or other regulations. In this context, the provided Python module serves as a powerful tool to ensure that patron data remains confidential and protected while still being usable for certain types of analytical use.
Pseudonymization is a data protection technique where personally identifiable information (PII) fields within a data record are replaced with artificial identifiers or pseudonyms. This method ensures data subject privacy while still permitting data analysis; given proper keys and credentials, aspects of the data set may be decrypted for closer analysis while still maintaining the integrity of both the data and the privacy of patrons.
The HybridPatronPseudonymizer is a Python class that implements a hybrid encryption system. Hybrid encryption combines the benefits of both symmetric and asymmetric encryption.