Graphics Processing Units (GPUs) have become indispensable for accelerating compute-intensive applications across various domains. To meet the surging demand for GPU computing power, many cloud service providers now offer GPU-as-a-Service (GPUaaS) through virtualization technologies that allow multiple users to efficiently share physical GPU resources. However, the security implications of GPU sharing, particularly the risks of sensitive information leakage between co-resident tenants, remain largely unexplored. This project undertakes a pioneering effort to understand and mitigate such risks. The project's novelties are the first investigation of previously unknown side-channel threats in GPUaaS, a comprehensive measurement study of GPU resource sharing policies in real-world deployments, and the development of practical countermeasures easily adoptable in today's cloud infrastructure. The project’s broader significance and importance are stronger data-privacy guarantees for cloud users and actionable security guidance for the fast-growing GPUaaS market. The research in this project proceeds through three interconnected thrusts. The first thrust seeks to break new ground by uncovering the first practical side-channel attacks in GPUaaS, which exploit GPU microarchitectural components, specifically caches and translation lookaside buffers (TLBs), to expose sensitive information from virtual desktop users and extract the proprietary design of neural networks during their