Peruseristys

Peruseristys is a theoretical framework in information ethics and privacy engineering that emphasizes preserving user autonomy through deliberate isolation of data contexts. The central idea is that information collected in one setting should not automatically be transferable or linkable to other contexts, thereby reducing the risk of pervasive profiling and unwanted inference. Proponents argue that contextual separation supports meaningful consent and control over personal data by limiting cross-context data fusion.

The term is derived from Finnish roots, with perus meaning basic or fundamental and eristys meaning isolation or separation. It appeared in academic discussions within European privacy discourse in the 2010s as a descriptive notion rather than a formal standard, and has been used to contrast holistic data aggregation with context-aware design.

Core principles include data minimization, context-bound processing, modular architectures, and transparent governance by users or their proxies. Techniques associated with peruseristys range from siloed data stores and strict access controls to contextual consent models and privacy-preserving computation that preserves data usefulness while constraining linkage across domains.

Applications span data architecture, product design, and regulatory compliance, particularly in systems that handle sensitive information across multiple services. Critics argue that the approach can complicate data ecosystems, introduce performance overhead, and conflict with business models that rely on cross-context analytics. It may also face challenges in regulatory harmonization and in defining appropriate boundaries between contexts.

See also: data minimization, contextual integrity, privacy by design, data sovereignty.