Bibliographic record
Abstract
Progress in networking research depends crucially on applying novel analysis tools to real-world traces of network activity. This often conflicts with privacy and security requirements; many raw network traces include information that should never be revealed to others.The traditional resolution of this dilemma uses trace anonymization to remove secret information from traces, theoretically leaving enough information for research purposes while protecting privacy and security. However, trace anonymization can have both technical and non-technical drawbacks.We propose an alternative to trace-to-trace transformation that operates at a different level of abstraction. Since the ultimate goal is to transform raw traces into research results, we say: cut out the middle step. We propose a model for shipping flexible analysis code to the data, rather than vice versa. Our model aims to support independent, expert, prior review of analysis code. We propose a system design using layered abstraction to provide both ease of use, and ease of verification of privacy and security properties. The system would provide pre-approved modules for common analysis functions. We hope our approach could significantly increase the willingness of trace owners to share their data with researchers. We have loosely prototyped this approach in previously published research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.339 | 0.213 |
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".