Practical Robust Communication in DHTs Tolerating a Byzantine Adversary
Bibliographic record
Abstract
There are several analytical results on distributed hash tables (DHTs) that can tolerate Byzantine faults. Unfortunately, in such systems, operations such as data retrieval and message sending incur significant communication costs. For example, a simple scheme used in many Byzantine fault-tolerant DHT constructions of n nodes requires O(log3n) messages, this is likely impractical for real-world applications. The previous best known message complexity is O(log2n) in expectation, however, the corresponding protocol suffers from prohibitive costs owing to hidden constants in the asymptotic notation and setup costs. In this paper, we focus on reducing the communication costs against a computationally bounded adversary. We employ threshold cryptography and distributed key generation to define two protocols both of which are more efficient than existing solutions. In comparison, our first protocol is deterministic with O(log3n) message complexity and our second protocol is randomized with expected O(log n) message complexity. Further, both the hidden constants and setup costs for our protocols are small and no trusted third party is required. Finally, we present results from micro benchmarks conducted over PlanetLab showing that our protocols are practical for deployment under significant levels of churn and adversarial behaviour.
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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 | 0.017 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".