🤖 AI Summary
This work addresses the challenge of ensuring availability and tamper resistance in evidence-collection infrastructures for continuous physical processes—such as authorship verification—even when the prover controls the environment. We propose the first continuous-process attestation architecture based on Trusted Execution Environments (TEEs), which leverages hardware isolation to defend against “trust inversion” attacks. The design includes a resilient evidence-chain protocol to handle TEE crashes, network partitions, and enclave migration, along with a tiered input assurance mechanism (Tier 1–3) and fast sealed-state recovery. Our implementation on Intel SGX demonstrates that per-checkpoint CPU overhead remains below 25% (under 0.3% at 30-second intervals), Monte Carlo simulations show evidence-chain availability exceeding 99.5%, and state recovery completes in under 200 milliseconds.
📝 Abstract
Process attestation systems verify that a continuous physical process, such as human authorship, actually occurred, rather than merely checking system state. These systems face a fundamental dependability challenge: the evidence collection infrastructure must remain available and tamper-resistant even when the attesting party controls the platform. Trusted Execution Environments (TEEs) provide hardware-enforced isolation that can address this challenge, but their integration with continuous process attestation introduces novel resilience requirements not addressed by existing frameworks. We present the first architecture for continuous process attestation evidence collection inside TEEs, providing hardware-backed tamper resistance against trust-inverted adversaries with graduated input assurance from software-channel integrity (Tier 1) through hardware-bound input (Tier 3). We develop a Markov-chain dependability model quantifying Evidence Chain Availability (ECA), Mean Time Between Evidence Gaps (MTBEG), and Recovery Time Objectives (RTO). We introduce a resilient evidence chain protocol maintaining chain integrity across TEE crashes, network partitions, and enclave migration. Our security analysis derives formal bounds under combined threat models including trust inversion and TEE side channels, parameterized by a conjectural side-channel leakage bound esc that requires empirical validation. Evaluation on Intel SGX demonstrates under 25% per-checkpoint CPU overhead (<0.3% of the 30 s checkpoint interval), >99.5% Evidence Chain Availability (ECA) (the fraction of session time with active evidence collection) in Monte Carlo simulation under Poisson failure models, and sealed-state recovery under 200 ms.