Securely Reinforcing Synchronization for Embedded Online Contests
Bibliographic record
Abstract
When competing in eBay bidding, online games, or e-exams in embedded computing environments, people naturally face asynchronous starts from different computing devices, which is treated as a security risk of online contests. The security risks of online contests also include eavesdropping during data transmission without intended rights, and false starts by malicious competitors, which also means asynchrony in contests. Accordingly, online contests need security guarantees, especially on synchronization. In this article, for synchronic and secure starts in a contest, we update security requirements of confidentiality, anonymity, and synchrony, comparing the current work to our previous work. Based on the updated requirements, we propose a general framework for the Advanced Secure Synchronized Reading (ASSR) system, which can hold multiple contests simultaneously in the cloud. It is important to note that the system can ignore the impacts of heterogeneity among competitors. Considering the heterogeneity both on transmission and computing, we construct a novel Randomness-reused Identity Based Key Encapsulation Mechanism (RIBKEM) to support separable decapsulation, which can shorten both decryption delay and transmission delay with the best efforts. Finally, ASSR enhances synchronization achievement for contest starts with heterogeneous delays of competitors while satisfying other security requirements. As a complement, the analysis on the provable security of ASSR is given, as well as a further analysis on the achievement of synchronization.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".