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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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