Constraint-Based Privacy Preserving-Path Computation Element
Bibliographic record
Abstract
Information privacy and protection is fundamental in the context of path computation. When a path computation client (PCC) requests the shortest path between two nodes from a path computation element (PCE), it desires to do so while protecting the sensitive information carried by the query as well as the overall topology of the network. The authors provide a novel framework to compute the shortest path, between a source and a destination, subject to a constraint, represented in the case by a required minimum bandwidth, while preserving the privacy of both client and server. By employing a secure homomorphic encryption scheme, the PCE can blindly compute the path while being oblivious to the content of the encrypted queries. The output of the PCE computation is an encrypted path that is only decipherable by its original secret key. The implementation using the homomorphic scheme over the integers from Van Dijk, Gentry, Halevi, and Vaikuntanathan (DGHV) shows promising results that the authors analyze in detail throughout this paper.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".