SwiftParade: Anti-Burst Multipath Validation
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Bibliographic record
Abstract
Path validation promises a necessary security add-on for future Internet architectures. It authenticates not only source identities but also the exact path where a packet forwards through. This offers users more flexibility and reliability in network services. Most existing solutions focus on single-path validation that pre-correlates a packet to a specific forwarding path. However, parallel transmissions in multipath routing tend to induce bursty traffic that is hardly validated in time by existing solutions. In this paper, we present SwiftParade as the first attempt toward anti-burst multipath validation. It proposes a composite validation technique that can simultaneously validate a group of packets likely from multiple different paths. This helps to amortize the validation overhead across packets of the entire group instead of imposing the validation overhead equally on every packet. To implement composite validation, SwiftParade further explores a noncommutative homomorphic asymmetric encryption scheme. We prove effectiveness and security of SwiftParade through theoretical analysis. We also conduct extensive experiments to evaluate SwiftParade performance. The results show that SwiftParade offers high efficiency and applicability to multipath validation with complex routing topologies. In comparison with the state-of-the-art multipath validation solution—Atlas, SwiftParade speeds up packet processing by 2.5× <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 8.3\times$</tex-math></inline-formula> and increases communication throughput by 2.8× <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 10.2\times$</tex-math></inline-formula> .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it