Pseudonymization
Pseudonymization is a data protection technique that replaces personally identifiable information (PII) with artificial identifiers or pseudonyms, making data less identifiable while retaining its analytical utility. It is a reversible process where the original data can be restored using a separate key or mapping, distinguishing it from anonymization, which is irreversible. This method is commonly used in data processing, research, and compliance with privacy regulations like GDPR to balance data utility with privacy protection.
Developers should learn pseudonymization when handling sensitive data in applications, such as in healthcare, finance, or user analytics, to comply with privacy laws like GDPR, HIPAA, or CCPA, which require data minimization and protection. It is essential for scenarios where data needs to be processed or shared for analysis while reducing privacy risks, such as in machine learning datasets or database backups. By implementing pseudonymization, developers can enhance data security, avoid legal penalties, and build trust with users by safeguarding their personal information.