Mind the Gap: Ceremonies for Applied Secret Sharing
Bibliographic record
Abstract
Abstract Secret sharing schemes are desirable across a variety of real-world settings due to the security and privacy properties they can provide, such as availability and separation of privilege. However, transitioning secret sharing schemes from theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases. We present a formalization and analysis, using Ellison’s notion of ceremonies, that demonstrates how simple variations in use cases of secret sharing schemes result in the potential loss of some security properties, a result that cannot be derived from the analysis of the underlying cryptographic protocol alone. Our framework accounts for such variations in the design and analysis of secret sharing implementations by presenting a more detailed user-focused process and defining previously overlooked assumptions about user roles and actions within the scheme to support analysis when designing such ceremonies. We identify existing mechanisms that, when applied to an appropriate implementation, close the security gaps we identified. We present our implementation including these mechanisms and a corresponding security assessment using our framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".