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In recent years, Graphical Processing Unit (GPU) is not only becoming a piece of hardware that accelerates graphics but also playing a key role in accelerating the fields of machine learning and artificial intelligence. The GPU’s heightened importance has led to increasing concern about the confidentiality of a GPU’s computing data as well as its internal communications. Although the GPU Trusted Execution Environment (TEE) has been implemented as a solution toward this issue, side-channel attacks in GPUs still remain as an open problem. In this work, we introduce Delay-masquerading Technique Upheld StrongBox (DTUBox) to strengthen the resilience of existing GPU TEE over StrongBox against side-channel attacks by injecting obfuscated noise with our developed algorithm, making the correlations difficult to reference between a task and workload. In our evaluation, we demonstrate that with only around 5 …