Netinfo Security ›› 2025, Vol. 25 ›› Issue (11): 1658-1672.doi: 10.3969/j.issn.1671-1122.2025.11.002

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Heterogeneous CPU-GPU System Confidential Computing Survey

HAO Meng1, LI Jiayong1(), YANG Hongwei1, ZHANG Weizhe1,2,3   

  1. 1. School of Cyberspace Science, Harbin Institute of Technology, Harbin 150001, China
    2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
    3. Peng Cheng Laboratory, Shenzhen 518055, China
  • Received:2025-07-20 Online:2025-11-10 Published:2025-12-02

Abstract:

With the widespread adoption of data-intensive applications such as artificial intelligence, heterogeneous computing systems centered on CPU and GPU have become essential infrastructure. However, in untrusted environments such as cloud and edge computing, sensitive data face severe security threats during processing, which cannot be effectively mitigated by traditional encryption methods. Confidential computing, leveraging hardware-based trusted execution environments (TEE), provides an effective mechanism for protecting data in use. Nevertheless, existing technologies have primarily focused on CPU. Extending TEE security boundaries seamlessly to GPU, the core of modern computing engines, has therefore become a major focus of both academic and industrial research. This paper provided a comprehensive review of confidential computing technologies in CPU-GPU heterogeneous systems. It first revisited the fundamental concepts of confidential computing and analyzed representative attack vectors targeting GPU. Subsequently, existing GPU confidential computing solutions were categorized into three paradigms: hardware-assisted, hardware-software co-design, and software-based approaches. Finally, the key challenges in this domain were summarized, and potential directions for future research were discussed.

Key words: confidential computing, trusted execution environment, heterogeneous computing, GPU

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