Balancing Utility and Privacy

• contradictory ideals, but enable each other

summarize ideas by Jansen, Borgert, and Elson (2025)

Measuring Privacy

• Calculate privacy levels (k-anonymity) and compare to the value measured before

• Red teaming data anonymization (Jansen et al. 2026)

Include exercise on calculating the privacy level with k-anonymity like before in 2_1_Planning Privacy

Measuring Utility

Provide overview of utility measurements and maybe try out one

• utility indices (Carvalho et al. 2023)
  • predictive performance measures for machine learning (potentially as call-out box)
  • information loss measures:
    • distance/distribution comparisons
    • a penalty of transformations through generalisation and suppression
    • statistical differences
• assessing utility for the current use case
  • perform statistical analysis before and after anonymization
  • see if results are comparable

Include exercise for calculating one information loss measure

Striking the Right Balance

• iterative process: rework anonymization after measuring both

• explain the norms for acceptable values of privacy and utility indicators

• People to contact at LMU (data stewards? open science team?)

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

• apply measurement of k-anonymity

• apply measurement of utility

Resources, Links, Examples

• examples in UK: https://ukdataservice.ac.uk/deposit-data/sharing-experiences/

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.

Jansen, Luisa, Nele Borgert, and Malte Elson. 2025. “On the Tension Between Open Data and Data Protection in Research.” April 7, 2025. https://doi.org/10.31234/osf.io/5jt3s_v2.

Jansen, Luisa, Tim Ulmann, Robine Jordi, and Malte Elson. 2026. “Putting Privacy to the Test: Introducing Red Teaming for Research Data Anonymization.” arXiv. https://doi.org/10.48550/arXiv.2601.19575.