An Efficient and Secure User Revocation Scheme in Mobile Social Networks
Bibliographic record
Abstract
Mobile social network (MSN) is a promising networking and communication platform for users having similar interests (or attributes) to connect and interact with one another. For many recently introduced secure MSN data communication schemes, attribute-based encryption is often adopted to preserve user privacy and prevent outside attackers from eavesdropping. In this paper, we propose an efficient and secure user revocation scheme to address inside attacks based on an attribute-based encryption technique. The proposed scheme enables a trusted authority (TA) to flexibly control the data decryption capability of mobile social users. It disables malicious users from decrypting any data packet. As a result, proper user behavior is encouraged, inside attacks are reduced, and network security is enhanced. Through the analysis, we demonstrate that the proposed user revocation scheme is able to resist attribute collusion attacks and revoke collusion attacks. Extensive simulation results further confirm that the proposed scheme has much smaller communication overhead and much shorter delay than the existing solution [1].
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".