SocialSDN: Design and Implementation of a Secure Internet Protocol Tunnel Between Social Connections
Bibliographic record
Abstract
End-to-end encrypted (E2EE) network services can be classified into 1) network services that provide native endto-end encryption and 2) non-encrypted services transported through secure tunnels. While the first solution of native E2EE applications lacks generality and standardization, the second option of secure tunnels shows itself to be a promising solution, yet the current state-of-the-art still possesses several drawbacks. Primarily, the current state-of-the-art for establishing a secure tunnel for arbitrary IP traffic between two or more users requires significant technical expertise. Secondly, due to side-channel effects, the current state-of-the-art for cryptographically protected network tunnels may leak sensitive information through traffic pattern analysis. Lastly, the current state-of-the-art for this type of networking lacks elegance and convenience and therefore users often settle for less secure non-E2EE services. In this paper, we present SocialSDN which utilizes concepts from social networking and software-defined networking to build a tool which addresses many of the issues holding back mass adoption of E2EE network services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".