minimalinformation

Minimalinformation is a term used in information theory and applied disciplines to describe the principle of using and disclosing the smallest amount of information necessary to achieve a given objective. As a concept, it overlaps with data minimization, efficient encoding, and privacy by design, and it is often discussed in the contexts of communication protocols, data governance, and machine learning.

In information-theoretic terms, minimalinformation can be viewed as the pursuit of representations or disclosures that are sufficient to make correct inferences or enable a task while excluding superfluous detail. This aligns with ideas such as minimal sufficient statistics, which aim to retain all information relevant to a decision while discarding irrelevant data. In practice, minimalinformation manifests as actions like returning only essential fields in an API response, limiting telemetry, or anonymizing identifiers to prevent unnecessary exposure.

Applications include privacy-preserving data handling, secure protocol design, and resource-efficient systems. For example, a web service may answer a query with the minimum data needed to satisfy the request, or a dataset may be released with features reduced to the least informative subset that preserves utility.

Limitations include the trade-off between utility and privacy, where too aggressive minimization can degrade performance or distort results. Achieving truly minimal information often requires formal modeling of the task, clear usefulness criteria, and careful assessment of potential information leakage. Related concepts include data minimization, minimal sufficient statistics, and information-theoretic encoding.