Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.
Bibliographic record
Abstract
We present Shechi, an easy-to-use programming framework for secure high-performance computing on distributed datasets. Shechi automatically converts Pythonic code into a secure distributed equivalent using multiparty homomorphic encryption (MHE), combining homomorphic encryption (HE) and secure multiparty computation (SMC) techniques to enable efficient distributed computation. Shechi abstracts away considerations about the private and distributed aspects of the input data from end users through a familiar Pythonic syntax. Our framework introduces new data types for the efficient handling of distributed data as well as systematic compiler optimizations for cryptographic and distributed computations. We evaluate Shechi on a wide range of applications, including principal component analysis and complex genomic analysis tasks. Our results demonstrate Shechi's ability to uncover optimizations missed even by expert developers, achieving up to 15× runtime improvements over the prior state-of-the-art solutions and a 40-fold improvement in code expressiveness compared to code manually optimized by experts. Shechi represents the first MHE compiler, extending secure computation frameworks to the analysis of sensitive distributed datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".