Privacy-enhancing technologies (PETs)
Privacy-enhancing technologies (PETs) are software, hardware and methodological solutions that protect personal data by minimising identifiability, limiting disclosure or enabling computation without revealing underlying data, while preserving the utility of the data for legitimate purposes. The EDPB, ENISA, the OECD and the UK ICO have each published PET taxonomies and guidance, and the GDPR refers to several PETs as examples of data protection by design and security measures (Art. 25 and 32).
The main families are: pseudonymisation and tokenisation; encryption at rest, in transit and in use (homomorphic encryption, trusted execution environments); anonymisation techniques such as k-anonymity, generalisation and differential privacy; federated learning and secure multi-party computation, which allow models or statistics to be computed across institutions without pooling raw data; synthetic data generation; and zero-knowledge proofs for authentication without disclosure.
PETs are not a legal category and do not by themselves take data outside the GDPR: pseudonymised and encrypted data remain personal data, and whether a synthetic or differentially private dataset is anonymous must be assessed case by case. Their value lies in reducing risk, which lowers the DPIA risk score, supports supplementary measures for international transfers, and enables secondary use of health data through health data warehouses and the European Health Data Space. Regulators expect their use to be documented and their limits understood.
