Identifierilla

Identifierilla is a term used to describe a theoretical framework for creating stable, privacy-preserving identifiers across digital systems. It envisions identifiers derived from a controlled combination of non-identifying attributes and cryptographic tokens, enabling cross-domain linkage without disclosing raw personal data.

The framework emphasizes deterministic generation, enabling the same input attributes to produce the same Identifierilla ID across trusted domains, subject to governance policies. It relies on attribute normalization, per-source salts, and cryptographic hashing, with revocation and auditing capabilities. A policy module governs eligibility, attribute selection, and data retention, allowing organizations to enforce privacy requirements and minimize exposure risk.

The architecture typically includes an Identifierilla engine, an attribute set, a policy layer, a secure mapping store, and an access controller. Data from source systems are normalized, filtered for sensitive fields, and combined with a per-source salt before hashing to produce the Identifierilla identifier. The resulting ID can be used for cross-system matching, analytics, or federated identity workflows while limiting exposure of underlying attributes.

Common use cases include cross-platform user matching for marketing analytics, patient record linkage in privacy-conscious healthcare environments, and federated identity schemes where central identity services are restricted. While appealing for privacy, Identifierilla requires strong governance, risk assessment, and performance considerations. Potential limitations include residual re-identification risk, reliance on trustworthy attribute sources, and the need for interoperable standards. See also privacy-preserving data linkage, pseudonymous identifiers, and tokenization.