Comparison of machine learning models applied on anonymized data with
  different techniques
Judith Sáinz-Pardo Díaz Álvaro López García
Anonymization techniques based on obfuscating the quasi-identifiers by means
of value generalization hierarchies are widely used to achieve preset levels of
privacy. To prevent different types of attacks against database privacy it is
necessary to apply several anonymization techniques beyond the classical
k-anonymity or $\ell$-diversity. However, the application of these methods is
directly connected to a reduction of their utility in prediction and decision
making tasks. In this work we study four classical machine learning methods
currently used for classification purposes in order to analyze the results as a
function of the anonymization techniques applied and the parameters selected
for each of them. The performance of these models is studied when varying the
value of k for k-anonymity and additional tools such as $\ell$-diversity,
t-closeness and $\delta$-disclosure privacy are also deployed on the well-known
adult dataset.