Anonymous and Distributed Authentication for Peer-to-Peer Networks
Bibliographic record
Abstract
Well-known authentication mechanisms such as Public-key Infrastructure (PKI) and Identity-based Public-key Certificates (ID-PKC) are not suitable for integration in a Peer-to-Peer (P2P) network environment, the reason being either the lack of or the difficulty in maintaining a centralized authority to manage the certificates.Authentication becomes even harder in anonymous environments.In this study, we present three authentication protocols such that the users can authenticate themselves in an anonymous P2P network, without revealing their identities.The first protocol uses existing ring signature schemes to obtain anonymous authentication, the second is an anonymous authentication protocol utilizing secret sharing schemes, and lastly a zero-knowledge-based anonymous authentication protocol.We provide security justifications for the three aforementioned protocols in terms of anonymity, completeness, soundness, resilience to impersonation attacks, and resilience to replay attacks.We also provide examples of conceptual topologies and how the peers would behave and rearrange in case of failure.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".