Perattribute

Perattribute is a term used in data modeling and software design to describe an approach that treats and processes each attribute of a data object independently rather than applying operations uniformly across the entire record. It emphasizes attribute-level granularity in validation, transformation, security, and analytics.

In practice, perattribute design means defining behavior and policies at the attribute level, with explicit rules for each field such as format validation, masking, encryption, or auditing. This approach contrasts with per-object policies, where a single rule applies to all attributes of a record and may obscure field-specific requirements.

Common use cases include per-attribute validation, where each field has its own constraints; per-attribute encryption, where different fields use distinct encryption keys or schemes; and per-attribute access control, where permissions vary by attribute rather than by the whole object. It also appears in analytics and data integration workflows, where attributes may undergo different transformations or enrichments.

Benefits of perattribute design include greater flexibility, improved data quality through field-specific rules, and finer-grained auditability. Drawbacks include increased design and maintenance complexity, potential performance overhead, and a greater need for robust metadata to manage attribute dependencies, evolution, and interoperability between systems.

Examples often cited involve customer data with separate handling for personal identifiers (which may be masked or encrypted) versus non-sensitive attributes (which may be freely transformed). Perattribute concepts intersect with related ideas such as attribute-level encryption, attribute-based access control, and schema evolution, offering a modular alternative to uniform, all-or-nothing processing of records.