New Methods to Protect Privacy When Using Patient Health Data to Compare Treatments

Xiong, Li; Post, Andrew A.; Jiang, Xiaoqian; Ohno-Mochado, Lucila

doi:10.25302/02.2021.me.131007058

Cited by 1 publication

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“…In the biomedical field, DP is extensively employed in data query systems; the noise integrated into query responses helps protect sensitive inquiries pertaining to uncommon cases [8,9]. Current research in DP focuses on solving complex problems such as determining optimal privacy budgets and noise levels to balance confidentiality with data utility [8,10,11].…”

Section: Introductionmentioning

confidence: 99%

Exploring the tradeoff between data privacy and utility with a clinical data analysis use case

Im,

Kim,

Lee

et al. 2024

BMC Med Inform Decis Mak

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Background Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset’s utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset’s utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility. Methods Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two. Results All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores. Conclusions As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data’s intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

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