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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 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".