Bibliographic record
Abstract
This paper resolves an open problem raised by Blocki et al. (FOCS 2012), i.e., whether other variants of the Johnson-Lindenstrauss transform preserves differential privacy or not? We prove that a general class of random projection matrices that satisfies the Johnson-Lindenstrauss lemma also preserves dif-ferential privacy. This class of random projection matrices requires only n Gaussian samples and n Bernoulli trials and allows matrix-vector multiplication in O(n log n) time. In this respect, this work un-conditionally improves the run time of Blocki et al. (FOCS 2012) without using the graph sparsification trick of Upadhyay (ASIACRYPT 2013). For the metric of measuring randomness, we stick to the norm used by earlier researchers who studied variants of the Johnson-Lindenstrauss transform and its applica-tions, i.e., count the number of random samples made. In concise, we improve the sampling complexity by quadratic factor, and the run time of cut queries by an O(no(1)) factor and that of covariance queries by an O(n0.38) factor. Our proof for both the privacy and utility guarantee uses several new ideas. In order to improve the dimension bound, we use some known results from the domain of statistical model selection. This makes our proof short and elegant, relying just on one basic concentration inequality. For the privacy proof, even though our mechanism closely resembles that of Blocki et al. (FOCS 2012) and Upadhyay (ASIACRYPT 2013), we cannot use their proof idea. This is because the projection matrices we are inter-ested in introduces non-trivial correlations between any two rows of the published matrix, and, therefore, we cannot invoke the composition theorem of Dwork, Rothblum and Vadhan (STOC 2009). We argue that the published matrix is not r-multivariate distribution; rather one matrix-variate distribution. We compute the distribution of the published matrix and then prove it preserves differential privacy.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".