Mutual impact of feature selection and privacy-preserving mechanisms

Abstract Privacy concern has gained increased attention in data analysis, prompting the application of privacy-preserving methodologies. This includes private dataset generation techniques designed to conceal sensitive information, such as anonymization, Differential Privacy (DP), Generative Adversarial Networks (GANs), and Differentially Private GANs (DPGANs). Nonetheless, the utilization of these techniques can influence the importance of features within the privatized dataset, potentially impacting the accuracy and dependability of subsequent data analysis and machine learning models. This study presents a comprehensive and detailed comparative examination to explore the preservation of features’ significance between the privatized dataset and its original counterpart, thus addressing the challenge of information hiding in privacy-preserving techniques. Through a series of experiments, we aim to offer valuable insights into the application of private data generating techniques to uphold the relevance of features, thereby advancing privacy-conscious data analysis across diverse applications.