ShEF: Shielded Enclaves for Cloud FPGAs
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Abstract (arXiv)
FPGAs are now used in public clouds to accelerate a wide range of applications, including many that operate on sensitive data such as financial and medical records. We present ShEF, a trusted execution environment (TEE) for cloud-based reconfigurable accelerators. ShEF is independent from CPU-based TEEs and allows secure execution under a threat model where the adversary can control all software running on the CPU connected to the FPGA, has physical access to the FPGA, and can compromise the FPGA interface logic of the cloud provider. ShEF provides a secure boot and remote attestation process that relies solely on existing FPGA mechanisms for root of trust. It also includes a Shield component that provides secure access to data while the accelerator is in use. The Shield is highly customizable and extensible, allowing users to craft a bespoke security solution that fits their accelerator's memory access patterns, bandwidth, and security requirements at minimum performance and area overheads. We describe a prototype implementation of ShEF for existing cloud FPGAs, map ShEF to a performant and secure storage application, and measure the performance benefits of customizable security using five additional accelerators.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1.
4 papers.
- 2022Machine Learning with Confidential Computing: A Systematization of Knowledgecited by 13
- 2022Confidential Machine Learning within Graphcore IPUscited by 2
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
- 2024Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Acceleratorscited by 1
- 2024Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators4 Sep 2024
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference9 Sep 2023
- 2022Machine Learning with Confidential Computing: A Systematization of Knowledge22 Aug 2022
- 2022Confidential Machine Learning within Graphcore IPUs18 May 2022