Mohammed Aldeen, Sisheng Liang, Zhenkai Zhang, Linke Guo (Clemson University), Zheng Song (University of Michigan – Dearborn), and Long Cheng (Clemson University)

—Graphics processing units (GPUs) on modern computers are susceptible to electromagnetic (EM) side-channel attacks that can leak sensitive information without physical access to the target device. Website fingerprinting through these EM emanations poses a significant privacy threat, capable of revealing user activities from a distance. This paper introduces EMMasker, a novel software-based solution designed to mitigate such attacks by obfuscating the EM signals associated with web activity. EMMasker operates by generating rendering noise within the GPU using WebGL shaders, thereby disrupting the patterns of EM signals and confounding any attempts at identifying user online activities. Our approach strikes a balance between the effectiveness of obfuscation and system efficiency, ensuring minimal impact on GPU performance and user browsing experience. Our evaluation shows that EMMasker can significantly reduce the accuracy of state-of-the-art EM website fingerprinting attacks from average accuracy from 81.03% to 22.56%, without imposing a high resource overhead. Our results highlight the potential of EMMasker as a practical countermeasure against EM side-channel website fingerprinting attacks, enhancing privacy and security for web users.