Privacy Preservation of Multivariate Sensitive Data Using Hybrid Perturbation Technique

Technology is advancing rapidly nowadays. For this, a large amount of data is being stored in the cloud by tech giants and other organizations. These data include sensitive information like bank transactions, personal healthcare, etc. Whilst these data are used for mining by the data analysts for extracting valuable information, privacy might be breached. That’s why, it should be pre-processed before mining to preserve the privacy of the sensitive data. There are many existing methods to preserve either privacy or utility. However, if we use methods like those, we may lose the privacy of the data or its utility. And if the utility is lost, then it is useless for mining. To preserve both privacy and utility, a hybrid perturbation method called DA3RT is proposed in this study based on derivative, anti-derivative, and geometric rotation. The method is tested with six UCI dataset using three benchmark classifiers. Privacy has been tested with attack resistance, entropy, and other privacy metrics; utility has been tested using accuracy, F1-score, and AUC. The experiment exhibits that DA3RT can preserve privacy as well as utility better than the existing perturbation methods.