Customizable Utility-Privacy Trade-Off: A Flexible Autoencoder-Based Obfuscator

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.‎‎