ReDD: recommendation‐based data dissemination in privacy‐preserving mobile social networks
Bibliographic record
Abstract
Abstract Mobile social network (MSN), which is built upon popular smart phone devices and enables mobile users with similar interests to connect with one another, has received considerable attention in recent years. A common phenomenon that makes the MSN vivid today is mobile users often like to share attractive things to their friends in MSN. In this paper, based on this common phenomenon, we propose an efficient recommendation‐based data dissemination (ReDD) protocol for MSN, which can efficiently disseminate high‐quality messages in a privacy‐preserving way. Specifically, in the proposed ReDD protocol, each mobile user, based on both his or her own view and his or her friends' recommendations, will form his or her personal estimation on the quality of a message. Only if the estimation of quality reaches a threshold, the message will be disseminated. In this way, high‐quality messages can be widely spread in the network and occupy more network resources than low‐quality ones. In order to check the validity of friends' recommendations, ReDD also employs an efficient anonymous authentication technique, which ensures that only the friends of a user can verify recommendations made by the user. Detailed security analysis demonstrates that ReDD can effectively resist various attacks launched by attackers and ensure identity privacy of nodes, confidentiality of shared keys and integrity of data packets. In addition, extensive simulations are also conducted to evaluate the performance of ReDD in terms of the number of active nodes and the average active time, and the simulation results show that high‐quality messages can be disseminated widely and efficiently, while low‐quality ones will be eliminated shortly to avoid occupying network resources. Copyright © 2014 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".