Evaluating Confidential Computing for Research Replication: A Case Study Using Intel TDX

Abstract

Reproducibility in empirical social-science research is essential, yet replicating code and data from academic journals often demands complex, unsecured computing environments that editors cannot easily audit. Confidential computing technologies such as the Intel Trust Domain Extensions (TDX) hold promise to embed the entire workflow in a verifiable enclave, but their practical cost, performance, and compatibility have not been systematically evaluated. To test whether TDX can underpin scale replication, Azure and Google Cloud TDX virtual machines were launched and attempted to reproduce 2025 Management Science articles with a public or provided replication package. Five papers for which complete materials were available were evaluated. Packages spanned Stata, R, SAS, MATLAB/Octave, and hybrid toolchains; some relied on WRDS or other proprietary data sources, and Virtual Machine billing logs alongside wall-clock runtimes were tracked and recorded within the VM. Three packages generated outputs identical to those reported (60%), one failed owing to mission data or irreconcilable code errors (20%), and one remains in extended execution (20%). This yields a replication rate of ~60% at a median cost of $0.29 USD, a mean cost of $0.30 USD, a median runtime of 186 minutes, and a mean runtime of ~167.4 minutes. While based on a limited sample size, these findings highlight that TDX virtual machines can securely replicate most multi-language research workflows at a negligible incremental cost, positioning confidential computing as a viable pathway for journals to enforce reproducibility without exposing sensitive code or data.