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Information Loss Metrics

Meaning

Information Loss Metrics are quantitative measures used to precisely assess the reduction in data utility, accuracy, or granularity that results from applying privacy-enhancing techniques, such as generalization or suppression, to a sensitive health dataset. These metrics formally quantify the inevitable trade-off between the achieved level of privacy protection and the remaining usefulness of the de-identified data for clinical research, particularly when analyzing subtle endocrine relationships or metabolic pathways. The primary objective is to minimize information loss while still rigorously meeting a defined, acceptable privacy standard, such as k-anonymity or differential privacy.