You can lie but not deny: SWMR registers with signature properties in systems with Byzantine processes
Bibliographic record
Abstract
We define and show how to implement SWMR registers that provide properties of unforgeable digital signatures—without actually using such signatures—in systems with Byzantine processes. More precisely, we first define SWMR verifiable registers. Intuitively, processes can use these registers to write values as if they are "signed", such that these "signed values" can be "verified" by any process and "relayed" to any process. We give a signature-free implementation of such registers from plain SWMR registers in systems with n > 3f processes, f of which can be Byzantine. We also give a signature-free implementation of SWMR sticky registers from SWMR registers in systems with n > 3f processes. Once the writer p writes a value υ into a SWMR sticky register R, the register never changes its value. Note that the value υ can be considered "signed" by p: once p writes υ in R, p cannot change the value in R or deny that it wrote υ in R, and every reader can verify that p wrote υ just by reading R. This holds even if the writer p of R is Byzantine. We prove that our implementations are optimal in the number of Byzantine processes they can tolerate. Since SWMR registers can be implemented in message-passing systems with Byzantine processes and n > 3f [11], the results in this paper also show that one can implement verifiable registers and sticky registers in such systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".