A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification
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
Privacy preserving data mining is a substantial research area that aims at protecting the privacy of individuals while enabling to perform data mining techniques. In this study, we propose a secure protocol that fulfils the privacy restriction by combining homomorphic encryption with differential privacy and integrate this protocol into Holte’s One Rule which is a simple, but accurate and efficient classification algorithm. The proposed method allows a researcher to get the answers of his/her queries to build One Rule classifier by processing the encrypted training dataset under Paillier’s cryptosystem and also applies differential privacy to minimize the privacy leakage of individuals as much as possible in this training dataset. Therefore, both of security and privacy of the individuals in the training dataset for classification are provided thanks to our proposed method; since neither the parties, nor the researcher attain any information about the individuals in the database. Besides the One Rule classifier, we apply our proposed privacy preservation model to Naïve Bayes classification algorithm for the performance comparison, and show the efficiency of the proposed method through experiments on real data from UCI repository.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.006 | 0.006 |
| 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