Fine-Grained Identification with Real-Time Fairness in Mobile Social Networks
Bibliographic record
Abstract
Mutual user identification is a necessary step for trust establishment among users in an unattended mobile social network (MSN). Directly exposing identity information to others unknown may cause total unfairness in identity loss when the other party of the identification process misbehaves. Using an on-line trusted third party (TTP) for user identification will cause communication and security problems, while a traditional off-line TTP solution will generate delay in fairness enforcement. In this paper, we propose a novel fine-grained identification protocol, which provides confidentiality, unlinkability, and real-time fairness without the involvement of TTP. In the protocol, identification is carried out by an iterative identification information exchange process, where two participating users have to disclose part of their identification information to each other in each iteration. The process terminates whenever one of them fails to do so. In this way, if a user loses part of its identification information to another user, then it must have obtained an approximately equal amount of identification information of that user. Therefore, misbehavior is discouraged, and fairness is improved. Through analysis we demonstrate that fairness can be well guaranteed as long as users strictly follow the protocol rules. Extensive simulation results further confirm that the proposed protocol can significantly reduce fairness loss in MSN environment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".