TimeFabric: Trusted Time for Permissioned Blockchains
Bibliographic record
Abstract
As the popularity of blockchains continues to rise, blockchain platforms must be enhanced to support new application needs. In this paper, we propose one such enhancement that is essential for financial applications and online marketplaces - support for time-based logic such as verifying deadlines or expiry dates and examining a time window of recent account activity. We present a lightweight solution to reach consensus on the current time without relying on external time oracles. Our solution assigns timestamps to blocks at transaction validation time and maintains a cache reflecting the effects of recent transactions. We implement a proof-of-concept prototype, called TimeFabric, in Hyperledger Fabric, a popular permissioned blockchain platform, and experimentally demonstrate high throughput and minimal overhead (approximately 3%) of maintaining trusted time. We also demonstrate a 2x performance improvement due to the cache, compared to reconstructing account histories from the ledger.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".