Reducing Model Memorization to Mitigate Membership Inference Attacks
Bibliographic record
Abstract
Given a machine learning model and a record, membership inference attacks determine whether this record was used as part of the model’s training dataset. This can raise privacy issues. There is a desideratum to provide robust mitigation techniques against this attack that will not affect utility. One of the state-of-the-art frameworks in this area is SELENA, which has two phases: Split-AI and Self-Distillation to train a protected model. In this paper, we introduce a novel approach to the Split-AI phase, which tries to weaken the membership inference by using the Jacobian matrix norm and entropy. We experimentally demonstrate that our approach can decrease the memorization of the machine-learning model for three datasets: Purchase100, CIFAR-10, and SVHN, more than SELENA in the same range of utility in a setting in which we do not know any member of the training data.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".