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Keystone: An Open Framework for Architecting TEEs
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Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs with little room for customization. We present Keystone -- the first open-source framework for building customized TEEs. Keystone uses simple abstractions provided by the hardware such as memory isolation and a programmable layer underneath untrusted components (e.g., OS). We build reusable TEE core primitives from these abstractions while allowing platform-specific modifications and application features. We showcase how Keystone-based TEEs run on unmodified RISC-V hardware and demonstrate the strengths of our design in terms of security, TCB size, execution of a range of benchmarks, applications, kernels, and deployment models.
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TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE
A TEE-based on-device inference system that selects a small set of critical tensors and intermediate features to shield, matching the security of full-model shielding with up to 25.35x lower latency.
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