Balancing Utility and Privacy

summarize ideas by (Jansen2025_TensionOpenData?)

Measuring Utility

Provide overview of utility measurements and maybe try out one

  • utility indices (short, potentially as call-out box) (Carvalho et al. 2023)
    • call-out box on predictive performance measures for machine learning
    • information loss measures:
      • distance/distribution comparisons
      • a penalty of transformations through generalisation and suppression
      • statistical differences
  • after evaluation of data protection level and utility: re-work anonymization

Learning Objective

  • After completing this part of the tutorial, you will be able to make informed decisions when balancing the risks and utility of the anonymized data.

Exercises

none

To Do List

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References

Carvalho, Tânia, Nuno Moniz, Pedro Faria, and Luís Antunes. 2023. “Survey on Privacy-Preserving Techniques for Microdata Publication.” ACM Computing Surveys 55 (14s): 1–42. https://doi.org/10.1145/3588765.