Integrally Private Model Selection for Support Vector Machine

Today, there are unlimited applications of data mining techniques. According to ongoing privacy regulations, data mining techniques that preserve users’ privacy are a primary requirement. Our work contributes to the Privacy-Preserving Data Mining (PPDM) domain. We work with Integral Privacy, which provides users with private machine learning model recommendations and privacy against model comparison attacks. For machine learning, we work with Support Vector Machine (SVM), which is based on the structural risk minimization principle. Our experiments show that we obtain highly recurrent SVM models due to their peculiar properties, requiring only a subset of the training data to learn well. Not only high recurrence, but from our empirical results, we show that integrally private SVM models obtain good results in accuracy, recall, precision, and F1-score compared with the baseline SVM model and the \(\epsilon \) Differentially Private SVM (DPSVM) model.