| Autor: |
KAMPF, Marvin O., KRASKA, Detlef, PROKOSCH, Hans-Ulrich |
| Zdroj: |
Studies in Health Technology & Informatics; 2019, Vol. 258, p70-74, 5p, 2 Charts |
| Abstrakt: |
Background: To make patient care data more accessible for research, German university hospitals join forces in the course of the Medical Informatics Initiative. In a first step, the administrative data of university hospitals is made available for federated utilization. Project-specific de-identification of this data is necessary to satisfy privacy laws. Objective: We want to make a statement about the population uniqueness of the data. By generalizing the data, we try to reduce uniqueness and improve k-anonymity. Methods: We analyze quasi-identifying attributes of the Erlangen University Hospital's billing data regarding population uniqueness and re-identification risk. We count individuals per equality class (k) to measure uniqueness. Results: Because of the diagnoses and procedures being particularly unique in combination with sex and age of the patients, the data set is not anonymized in matters of k-anonymity with K>1. We are able to reduce population uniqueness with generalization and suppression of unique domains. Conclusion: To create k-anonymity with K>1 while still maintaining a particular utility of the data, we need to apply further established strategies of de-identification. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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