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
Massive graphs have become pervasive in a wide variety of data domains. However, they are generally more difficult to anonymize because the structural information buried in graph can be leveraged by an attacker to breach sensitive attributes. Furthermore, the increasing sizes of graph data sets present a major challenge to anonynization algorithms. In this paper, we will address the problem of privacy-preserving data mining of massive graph-data sets. We design a MapReduce framework to address the problem of attribute disclosure in massive graphs. We leverage the MapReduce framework to create a scalable algorithm that can be used for very large graphs. Unlike existing literature in graph privacy, our proposed algorithm focuses on the sensitive content at the nodes rather than on the structure. This is because content-centric perturbation at the nodes is a more effective way to prevent attribute disclosure rather than structural reorganization. One advantage of the approach is that structural queries can be accurately answered on the anonymized graph. We present experimental results illustrating the effectiveness of our method.
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.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.041 | 0.089 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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