PESCA: a peer‐to‐peer social network architecture with privacy‐enabled social communication and data availability
Bibliographic record
Abstract
The major challenge in current online social networks (OSNs) is privacy violation by OSN providers or unauthorised users. OSN providers collect unprecedented amounts of personal information for targeted advertising. Moreover, users are not able to share their social data with their friends with complete access control. Peer‐to‐peer (P2P) infrastructure is an interesting solution for a big‐brother‐free alternative to current OSN designs. However, the fundamental nature of P2P systems has dynamic peer turn‐over which results in data unavailability. Additionally, users’ data must be available in the OSN when authorised data audiences want to access them. For these reasons, we propose a P2P‐OSN architecture which is composed of a privacy enabled setup for users’ social communications and an adaptive replica placement strategy for ensuring availability for users’ shared data. The proposed framework correlates the availability of shared content in the P2P‐OSN to the access control assigned to them. Our evaluations show the proposed P2P‐OSN has considerable improvements in providing data privacy and availability compared with the existing approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".