Kampf M, Kraska D, Prokosch HU (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 258
Pages Range: 70-74
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.
APA:
Kampf, M., Kraska, D., & Prokosch, H.-U. (2019). An Analysis of Erlangen University Hospital's Billing Data on Utility-Based De-Identification. Studies in health technology and informatics, 258, 70-74.
MLA:
Kampf, Marvin, Detlef Kraska, and Hans-Ulrich Prokosch. "An Analysis of Erlangen University Hospital's Billing Data on Utility-Based De-Identification." Studies in health technology and informatics 258 (2019): 70-74.
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