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

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De-identifying patient data for privacy can harm its usefulness for analysis. This study shows balancing data privacy and utility is complex, requiring careful consideration of data use and user input.

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Area of Science:

  • Health Informatics
  • Data Privacy
  • Clinical Data Analysis

Background:

  • Data privacy is crucial for data utilization, but de-identification methods can reduce data utility.
  • Balancing data privacy and utility is challenging, with limited research on de-identification's impact on analysis results.

Purpose of the Study:

  • To demonstrate how de-identification methods affect dataset utility using a clinical use case.
  • To assess the feasibility of achieving a balance between data privacy and utility.

Main Methods:

  • Predictive modeling of emergency department length of stay using logistic regression on 1155 patient cases.
  • Generated 19 de-identified datasets using ARX software with varying configurations.
  • Compared variable distributions and prediction results between de-identified and original datasets.

Main Results:

  • All 19 de-identification scenarios reduced re-identification risk.
  • De-identification compromised dataset utility through record suppression and variable masking.
  • A significant correlation was found only between re-identification reduction rates and ARX utility scores.

Conclusions:

  • Effective privacy protection is vital for increasing health data analysis.
  • Achieving high privacy and utility requires understanding data use and involving data users.
  • A collaborative approach can help find a compromise between data privacy and utility.