Paradigm Shift in the Security-n-Privacy Implementation of Semi-Distributed Online Social Networking
Bibliographic record
Abstract
Social Networking Applications has gained tremendous response from all the sections of people across the entire world from last few years. Social networking has crossed all the boundaries and glued whole world population together. Users of OSN (Online Social Networking) sites can re-connect with school friends, find some activity or even life partners, and make new friends. OSN has also revolutionized the business community. Now the companies leverage OSN’s credibility and build their reputation, get invaluable information about the customers. The companies are also using OSN for the advertising and the recruitment processes. However, posting of user information on OSN poses greater threats/risks as identity theft, online stalking, and information leakage. The volume and accessibility of personal information available on social networking sites have attracted malicious people who seek to exploit this information. This imposes greater threat to the users’ privacy and security. In this article many security and privacy challenges currently faced by OSN applications are mentioned. The distributed OSN architecture with an external control module is proposed and a prototype is also presented which overcomes many of the privacy, security, accessibility and identity challenges in different perspectives faced by current OSN applications.
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 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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".