Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Abstract

Data warehouses are environments used for data analysis and efficient decision making within companies. They are tools that allow the execution of complex and multidimensional queries. One of the security vulnerabilities that can be used by malicious users is data inference, which is the deduction of private information by devious means. In the present work, we tried to show that the existence of functional dependencies in the data can help to perform an inference attack by using supervised learning algorithms to infer private information. These algorithms are Support Vector Machine (SVM), Random Forest (RF), Bayesian Regularized Neural Network (BRNN) and K-Nearest Neighbors (K-NN). The BRNN provided a better performance in our study. This paper implements an inference attack using regression learning algorithms, studies different dependency situations in the data, and uses the combination of COUNT, SUM, AVG and STDEV queries. The use of several methods in this study allows the prevention of inferences when one of these methods is used by a malicious user. We managed to achieve this attack by detecting 09.12% inferences on all methods compared to BRNN whose realized inference rate is 03.94%.