Towards Secure and Scalable Computation in Peer-to-Peer Networks
Bibliographic record
Abstract
We consider the problems of Byzantine agreement and leader election, where a constant fraction b < 1/3 of processors are controlled by a malicious adversary. The first problem requires that all uncorrupted processors come to an agreement on a bit initially held by one of the uncorrupted processors; the second requires that the uncorrupted processors choose a leader who is uncorrupted. Motivated by the need for robust and scalable computation in peer-to-peer networks, we design the first scalable protocols for these problems for a network whose degree is polylogarithmic in its size. By scalable, we mean that each uncorrupted processor sends and processes a number of bits that is only polylogarithmic in n. (We assume no limit on the number of messages sent by corrupted processors.) With high probability, our Byzantine agreement protocol results in agreement among a 1 - O(1/ln n) fraction of the uncorrupted processors. With constant probability, our leader election protocol elects an uncorrupted leader and ensures that a 1 - O(1/ln n) fraction of the uncorrupt processors know this leader. We assume a full information model. Thus, the adversary is assumed to have unlimited computational power and has access to all communications, but does not have access to processors' private random bits
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".