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Teamwork Makes TEE Work: Open and Resilient Remote Attestation on Decentralized Trust

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arxiv 2402.08908 v2 pith:H22DRGOV submitted 2024-02-14 cs.CR

classification cs.CR
keywords trustresilientjanusremoteattestationcentralizeddecentralizeddesign
verification ladder T0 review T1 audit T2 compute T3 formal

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Remote Attestation (RA) enables the integrity and authenticity of applications in Trusted Execution Environment (TEE) to be verified. Existing TEE RA designs employ a centralized trust model where they rely on a single provisioned secret key and a centralized verifier to establish trust for remote parties. This model is however brittle and can be untrusted under advanced attacks nowadays. Besides, most designs only have fixed procedures once deployed, making them hard to adapt to different emerging situations and provide resilient functionalities. Therefore, we propose JANUS, an open and resilient TEE RA scheme. To decentralize trust, we, on one hand, introduce Physically Unclonable Function (PUF) as an intrinsic root of trust (RoT) in TEE to directly provide physical trusted measurements. On the other hand, we design novel decentralized verification functions on smart contract with result audits and RA session snapshot. Furthermore, we design an automated switch mechanism that allows JANUS to remain resilient and offer flexible RA services under various situations. We provide a UC-based security proof and demonstrate the scalability and generality of JANUS by implementing an complete prototype.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Resilient Alerting Protocols for Blockchains

    cs.CR 2026-02 unverdicted novelty 7.0 of 10

    Blockchain alerting protocols achieve asymptotically optimal O(n²) bribery resistance via simultaneous games and timed commitments, with variants trading storage, time, and synchrony assumptions.

  2. Validation of GPU Computation in Decentralized, Trustless Networks

    cs.ET 2025-01 reject novelty 5.0 of 10

    Semantic similarity between LLM responses, with a fitted threshold of 0.5, separates same-model outputs from random responses and yields 76.5 percent held-out verification accuracy in a trusted-node setting.

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