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- Title:
Data Anonymization Techniques
- Author(s):
- Frank, Maximilian Josef
- Abstract:
- Removing identifiable information from research data is important to maintain the privacy of research participants. Unfortunately, this is often not enough to prevent deanonymization when the dataset is combined with other data. This recommendation shows some countermeasures against deanonymization and how to apply them.
- Keywords:
- Anonymization, K-Anonymity, L-Diversity, T-Closeness, Differential Privacy
- Month:
- Feb
- Year:
- 2024
- DOI:
- doi:10.14459/2024md1735453
- Language:
- en
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