ALPP: anonymous and location privacy preserving scheme for mobile IPv6 heterogeneous networks
Bibliographic record
Abstract
ABSTRACT The integration of mobile IPv6 heterogeneous networks enhances networking performance; however, it also breaks mobile node's anonymity and location privacy. In this paper, we propose an anonymous and location privacy preserving (ALPP) scheme that consists of two complementary subschemes: anonymous home binding update and anonymous return routability. In addition, anonymous mutual authentication and key establishment schemes have been proposed to work in conjunction with ALPP to authenticate a mobile node to its foreign gateway and create a shared key between them. ALPP adds anonymity and location privacy services to mobile IPv6 signaling to achieve mobile senders and receivers' privacy. Unlike existing schemes, ALPP alleviates the trade‐off between the networking performance and the achieved privacy level. Combining onion routing and anonymizer in ALPP scheme increases the achieved location privacy level where no entity in the network except the mobile node itself can identify this node's location. Using entropy model, we show that ALPP achieves higher degree of anonymity than the mix‐based scheme. The anonymous home binding update and anonymous return routability subschemes require less computation overheads and thwart both internal and external adversaries. Simulation results demonstrate that our schemes have low control packets routing delays and are suitable for the seamless handover. 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".