Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 16 2 2024 07 01 Customizable Utility-Privacy Trade-Off: A Flexible Autoencoder-Based Obfuscator 137 147 196000 10.22042/isecure.2024.422044.1037 EN Mohammad Ali Jamshidi Information Systems and Security Lab. (ISSL), Sharif University of Tech., Tehran, Iran 0009-0006-4433-0177 Mohammad Mahdi Mojahedian Information Systems and Security Lab‎. ‎(ISSL)‎, ‎Sharif University of Tech.‎, ‎Tehran‎, ‎Iran Mohammad Reza Aref Information Systems and Security Lab‎. ‎(ISSL)‎, ‎Sharif University of Tech.‎, ‎Tehran‎, ‎Iran Journal Article 2023 10 23 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.‎‎ https://www.isecure-journal.com/article_196000_945933005276eceda977997e902b9fd0.pdf