DEFF: a new architecture for private online social networks
Bibliographic record
Abstract
ABSTRACT In recent years, online social networks (OSNs) have had explosive growth in numbers and popularity. In an OSN, users communicate with each other and share information about themselves. However, limiting the flow of private information across OSNs is very important especially because most OSNs provide insufficient privacy settings to control information leakage. In this paper, we propose a mediated architecture for OSNs that protects users' information from both the OSN provider and unauthorized OSN users. Our proposed approach delegates most of the computation tasks to a semi‐trusted proxy server. We exploit a simplified broadcast encryption method in order to design a dynamic, efficient, flexible, and fine‐grained (DEFF) control system. In the proposed DEFF system, users are allowed to cryptographically categorize their friends into different relations and to share data with arbitrary groups of them. The results of our analysis indicate that the DEFF system fully protects users' privacy and is very efficient in terms of communication and computation complexities. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".