Taxing the Queue: Hindering Middleboxes From Unauthorized Large-Scale Traffic Relaying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When employed by online content providers, access-control policies can be evaded whenever clients masquerade behind a middlebox (MB) that meets the policies. An MB, commonly being the gateway of a virtual private network (VPN), typically contacts the content provider on behalf of the clients it colludes with, and relays the provider's outbound traffic to those clients. We propose a solution to hinder MBs from unauthorized relaying of traffic to a large number of clients. To the best of our knowledge, this is the first work to address this problem. Our solution increases the cost of collusion by leveraging client puzzles in a novel way, and uses network properties to help the content provider detect if its outbound traffic is being further relayed beyond a transport-layer connection. Our evaluation shows that the number of colluding clients follows a hyperbolic decay with the rate of creation of puzzles and the time required to solve a puzzle-both factors are influenced by the content provider, but grows almost linearly with the MB's computational resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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