To enhance the accuracy of learning models, it becomes imperative to train them on more extensive datasets. Unfortunately, access to such data is often restricted because data providers are hesitant to share their data due to privacy concerns. Hence, it is critical to develop obfuscation techniques that empower data providers to transform their datasets into new ones that ensure the desired level of privacy. In this paper, we present an approach where data providers utilize a neural network based on the autoencoder architecture to safeguard the sensitive components of their data while preserving the utility of the remaining parts. More specifically, within the autoencoder framework and after the encoding process, a classifier is used to extract the private feature from the dataset. This feature is then decorrelated from the other remaining features and subsequently subjected to noise. The proposed method is flexible, allowing data providers to adjust their desired level of privacy by changing the noise level. Additionally, our approach demonstrates superior performance in achieving the desired trade-off between utility and privacy compared to similar methods, all while maintaining a simpler structure. [ABSTRACT FROM AUTHOR]