Preserving User Privacy in Personalized Networks

Abstract

The advances in efficient computation and data processing technologies have ushered in an entirely new era in which network connectivity applications are being revolutionized by Artificial Intelligence (AI). One of the emerging AI applications in wireless networks is network personalization. Personalized wireless networks are designed to track user satisfaction levels using Machine Learning (ML) models that are trained and updated using context data collected from users. Nonetheless, user data collection and transmission make sensitive user information prone to various privacy attacks. Therefore, in this letter, we propose a privacy-preserving framework for personalized wireless networks. The proposed framework is enabled by Differential Privacy (DP), which is an influential privacy technology that offers a rigorous and provable privacy guarantee. Besides, the proposed framework is supported by a multi-stage prediction process, which adds a second layer of privacy protection to personal user data. Lastly, we investigate the impact of privacy budget and data size on the quality of user satisfaction predictors in personalized wireless networks.