Our Brothers' Keepers: Secure Routing with High Performance
Bibliographic record
Abstract
The Trinity (Brodsky et al., 2007) spam classification system is based on a distributed hash table that is implemented using a structured peer-to-peer overlay. Such an overlay must be capable of processing hundreds of messages per second, and must be able to route messages to their destination even in the presence of failures and malicious peers that misroute packets or inject fraudulent routing information into the system. Typically there is tension between the requirements to route messages securely and efficiently in the overlay. We describe a secure and efficient routing extension that we developed within the I3 (Stoica et al. 2004) implementation of the Chord (Stoica et al. 2001) overlay. Secure routing is accomplished through several complementary approaches: First, peers in close proximity form overlapping groups that police themselves to identify and mitigate fraudulent routing information. Second, a form of random routing solves the problem of entire packet flows passing through a malicious peer. Third, a message authentication mechanism links each message to it sender, preventing spoofing. Fourth, each peer's identifier links the peer to its network address, and at the same time uniformly distributes the peers in the key-space. Lastly, we present our initial evaluation of the system, comprising a 255 peer overlay running on a local cluster. We describe our methodology and show that the overhead of our secure implementation is quite reasonable.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".