Iditelnost

Iditelnost is a term used in Czech to refer to identifiability—the property that an object, data point, or entity can be uniquely recognized or distinguished within a given context. The concept is applied across disciplines, from mathematics and engineering to data privacy and information security, and is often closely related to the more formal term identifikovatelnost (identifiability).

In statistics and system identification, iditelnost describes whether model parameters can be uniquely determined from observed data. Structural identifiability concerns whether, in an ideal noiseless setting, different parameter values produce distinguishable model outputs. Practical identifiability takes into account real-world data quality, measurement errors, and data quantity. If a model is not identifiable, parameter estimates may be ambiguous or non-existent.

In data privacy, iditelnost relates to the risk that an individual can be re-identified from anonymized or pseudonymized datasets. Factors influencing identifiability include the amount of auxiliary information available, data granularity, and the number of records. Mitigation strategies include data minimization, perturbation, generalization, and approaches such as differential privacy to reduce re-identification risk.

In engineering and cybersecurity, the concept also covers the reliability of identity verification and authentication processes. Ensuring true identifiability of devices, users, and services is essential for access control, security auditing, and compliance with data protection requirements.

Overall, iditelnost describes how easily something can be uniquely identified within its context, with implications for model design, data ethics, and security.