🤖 AI Summary
This work addresses key challenges in academic reproducibility—namely insufficient editorial resources, difficulties in handling proprietary data, and inefficient workflows—by systematically introducing trusted execution environments (TEEs) for the first time. The authors propose a cloud-based reproducibility framework built on Intel TDX, wherein researchers execute their code within a TEE and submit cryptographically verifiable attestations of correct execution. Journals can then efficiently validate reproducibility without rerunning the code, ensuring data confidentiality, reducing author burden, and eliminating reliance on volunteer labor or scarce editorial resources. Theoretical analysis demonstrates that the approach is incentive-compatible and cost-effective; empirical evaluation confirms a low per-reproduction cost of only \$1.35–\$1.80, with minimal computational overhead and strong usability.
📝 Abstract
The growing replication crisis across disciplines such as economics, finance, and other social sciences as well as computer science undermines the credibility of academic research. Current institutional solutions -- such as artifact evaluations and replication packages -- suffer from critical limitations, including shortages of qualified data editors, difficulties in handling proprietary datasets, inefficient processes, and reliance on voluntary labor. This paper proposes a novel framework leveraging new technological advances in trusted-execution environments (TEEs) -- exemplified by Intel Trust Domain Extensions (TDX) -- to address the replication crisis in a cost-effective and scalable manner. Under our approach, authors execute replication packages within a cloud-based TEE and submit cryptographic proofs of correct execution, for which journals or conferences can efficiently verify without re-running the code. This reallocates the operational burden to authors while preserving data confidentiality and eliminating reliance on scarce editorial resources. As a proof of concept, we validate the feasibility of this system through field experiments, reporting a pilot study replicating published papers on TDX-backed cloud VMs, finding average costs of \$1.35--\$1.80 per package with minimal computational overhead relative to standard VMs and high success rates even for novice users with no prior TEE experience. We also provide a conduct formal analysis showing that TEE adoption is incentive-compatible for authors, cost-dominant for journals, and constitutes an equilibrium in the certification market. The findings highlight the potential of TEE technology to provide a sustainable, privacy-preserving, and efficient mechanism to address the replication crisis in academia.