Privacy preserving ID3 using Gini Index over horizontally partitioned data
Bibliographic record
Abstract
The ID3 algorithm is a standard, popular, and simple method for data classification and decision tree creation. Since privacy-preserving data mining should be taken into consideration, several secure multi-party computation protocols have been presented based on this technique. Entropy and Gini Index are two protocols which compute information-gain at each step when producing a decision tree. The Gini index, however, has been less studied in privacy-preserving data mining protocols. In this paper, we show how Gini can be used in privacy-preserving ID3 algorithms to create decision tree classifications in such a way that involved parties can jointly compute the gain value of each normal attribute without revealing their own private information to each other, while the database is horizontally partitioned over two or more parties. Three secure multiparty sub-protocols are presented to evaluate the intermediate computations. The communication overhead has been kept reasonably low to make the whole protocol efficient and practical.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".