Search results
Scholars focused on medical data privacy developed the ARX data anonymization tool, which offers k-anonymity functionality (Prasser and Kohlmayer, 2015). Similarly, researchers and data security practitioners worked towards improvingk-anonymity in regards to reducing
This paper analyzes the effects of k -anonymity in educational data for use in subsequent educational data mining methods (i.e., machine learning models in this case), and proposes ideas to improve the anonymization process in certain educational scenarios.
This paper analyzes the effects of k -anonymity in educational data for use in subsequent educa- tional data mining methods (i.e., machine learning models in this case), and proposes ideas to improve the anonymization process in certain educational scenarios.
A simple Python package to quickly run privacy metrics for your data. Obtain the K-anonimity, L-diversity and T-closeness to asses how anonymous your transformed data is, and how it's balanced with data usability.
Typical examples include k-anonymity, l-diversity, t-closeness or δ-presence. The basic idea of k-anonymity is to protect a dataset against re-identification by generalizing the attributes which could be used in a linkage attack (quasi identifiers).
1 gru 2021 · k-anonymity implies a manipulation of the values of QIDs for which several strategies exist (Gkoulalas-Divanis et al., 2014): generalisation (Samarati, 2001), suppression (Samarati, 2001), microaggregation (Domingo-Ferrer and Mateo-Sanz, 2002) and bucketisation (Xiao and Tao, 2006).
19 wrz 2024 · How it works. K-anonymity ensures that each individual in a dataset cannot be distinguished from at least k-1 other individuals with respect to the quasi-identifiers in the dataset. This is done through generalisation, suppression and sometimes top- and bottom-coding.
