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There has been considerable controversy regarding the accuracy and privacy of de-identification mechanisms used in the U.S. Decennial Census. We theoretically and experimentally analyze two such classes of mechanisms, swapping and differential privacy, especially examining their effects on ethnoracial minority groups. We first prove that the expected error of queries made on swapped demographic datasets is greater in sub-populations whose racial distributions differ more from the racial distribution of the global population. We also prove that the probability that m unique entries exist in a sub-population shrinks exponentially as the sub-population size grows. These properties suggest that swapping, which prioritizes unique entries, will produce poor accuracy for minority groups. We then empirically analyze the impact of swapping and differential privacy on the accuracy and privacy of a de- mographic dataset. We evaluate accuracy in several ways, including methods that stress the effect on minority groups. We evaluate privacy by counting the number of re-identified entries in a simulated linkage attack. Finally, we explore the disproportionate presence of minority groups in identified entries. Our empirical findings corroborate our theoretical results: for minority representation, the utility of differential privacy is comparable to the utility of swapping, while providing a stronger privacy guarantee. Swapping places a disproportionate privacy burden on minority groups, whereas an ϵ- differentially private mechanism is ϵ-differentially private for all subgroups.
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Exploring the tradeoff between data privacy and utility with a clinical data analysis use case
Abstract BackgroundSecuring 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. MethodsPredictive 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. ResultsAll 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. ConclusionsAs 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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- Award ID(s):
- 2124789
- PAR ID:
- 10529049
- Publisher / Repository:
- Springer Science and Business Media LLC
- Date Published:
- Journal Name:
- BMC Medical Informatics and Decision Making
- Volume:
- 24
- Issue:
- 1
- ISSN:
- 1472-6947
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s12911-024-02545-9
