שאלה | A hospital releases anonymized patient records for research. The dataset includes patients' age, city, and rare disease history. A researcher cross-references this data with publicly available voter registration records and successfully identifies a patient. Which anonymization principle was most likely not configured properly?

A hospital releases anonymized patient records for research. The dataset includes patients' age, city, and rare disease history. A researcher cross-references this data with publicly available voter registration records and successfully identifies a patient. Which anonymization principle was most likely not configured properly?

שאלה #169069

		K-Anonymity – The dataset had too few individuals in each group, making identification easy.
		L-Diversity – The dataset included sensitive attributes that were too uniform within each group.
		T-Closeness – The distribution of sensitive attributes (disease type) was too different from the general population.
		Differential Privacy – Noise was not properly added to the dataset.

If each group (age, city, disease) contains only one or very few individuals, it becomes possible to uniquely identify someone by cross-referencing with other available data. Proper K-Anonymity would require ensuring that each unique combination of quasi-identifiers (age, city) appears in at least K instances to prevent reidentification.

מיין לפי

ביו-אינפורמטיקה רפואית ביו-אינפורמטיקה רפואית

שאלה #169069

בקשה לשינוי התשובה / תיקון שאלה gavel

בקשה למחיקת השאלהmood_bad

בקשה לשינוי התשובה / תיקון שאלה

בקשה למחיקת השאלה