Pseudonymiseres

Pseudonymiseres are tools or processes that replace direct identifiers in data with pseudonyms to reduce identifiability. The original identifiers are detached from the data and stored in a separate, secured mapping. This approach allows authorized parties to re-link data to individuals when necessary, while keeping the primary data set less directly identifiable.

Common techniques used by pseudonymiseres include tokenization, deterministic hashing with a salt, and reversible encryption methods such as format-preserving encryption. Deterministic methods map the same input to the same pseudonym, enabling data linking across datasets; non-deterministic methods produce different pseudonyms to reduce the risk of linkage.

Uses for pseudonymiseres span sectors such as healthcare, finance, and marketing analytics. They support data analysis and operational workflows while limiting exposure of real identities. Re-identification remains possible if the mapping is compromised, if additional data sources are combined, or if key management and access controls fail, which is why strong governance is essential.

Regulatory and policy frameworks often view pseudonymisation as a privacy-protective technique. In many jurisdictions, pseudonymisation is described as processing that reduces identifiability but does not eliminate it entirely, requiring robust data governance. Effective pseudonymisation relies on secure key management, strict access controls, audit logs, and clearly defined roles and responsibilities.

Limitations and challenges include the dependency on the security of the mapping and keys, potential performance overhead, data governance complexity, and interoperability concerns across systems. It is not a universal solution and should be implemented as part of a layered privacy strategy.