PESCA: a peer‐to‐peer social network architecture with privacy‐enabled social communication and data availability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 it