Privacy-Preserving Bayesian Network for Horizontally Partitioned Data
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
Construction of learning structures for Bayesian networks is considered in this work when data is securely maintained by different parties, not willing to reveal their individual private data to each other. We propose a privacy-preserving protocol for Bayesian network from data which is homogeneously partitioned among two or more parties by using K2 algorithm, a heuristic algorithm typically used to construct Bayesian network. Three secure building blocks are also presented to use inside the main protocol; Secure Exponentiation, Secure Multi-party Factorial and Secure Product Comparison. We have also modified two existing building blocks which are used in this paper, Secure Multi-Party Addition and Multiplication, to improve their resistance against colluding attack. These protocols have the added advantage that they can even be used over public channels. That is, channels over which any party is able to see any messages exchanged between any two or more parties.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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