A Reputation-Enhanced Shard-Based Byzantine Fault-Tolerant Scheme for Secure Data Sharing in Zero Trust Human Digital Twin Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Secure data sharing is imperative in human digital twin (HDT) systems due to the continuous communication requirements among physical and virtual twins, making data security and privacy essential concerns. Previous works have emphasized the significance of blockchain technology in mitigating security challenges within digital twin systems. Nevertheless, existing blockchain-based solutions often fall short of meeting the specific latency and throughput demands of HDT systems, primarily attributed to the complicated consensus process of conventional blockchain solutions. As a result, this paper introduces a novel reputation-enhanced shard-based Byzantine fault-tolerant scheme designed for zero-trust HDT systems. We propose a parallel validation-based reputation-enhanced practical Byzantine fault tolerance consensus framework to address the need for improved throughput and reduced latency during data-sharing processes. This framework incorporates a priority-based block-appending process to prevent forking attacks, ensuring that critical aspects of the blockchain-enabled framework, such as security and decentralization, remain uncompromised. Moreover, we formalize the communication process among validators and their computation resource allocation as a Markov decision process. We then adopt the branching duelling Q-network approach to address the challenge posed by the large dimensions of the action space in our formulated problem. The results demonstrate that the proposed framework significantly enhances authentication, authorization, and validation processes in HDT through increased throughput and reduced latency, providing a robust solution for secure and efficient data sharing in HDT systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it