Engineering Methods for Differentially Private Histograms: Efficiency\n Beyond Utility
Bibliographic record
Abstract
Publishing histograms with $\\epsilon$-differential privacy has been studied\nextensively in the literature. Existing schemes aim at maximizing the utility\nof the published data, while previous experimental evaluations analyze the\nprivacy/utility trade-off. In this paper we provide the first experimental\nevaluation of differentially private methods that goes beyond utility,\nemphasizing also on another important aspect, namely efficiency. Towards this\nend, we first observe that all existing schemes are comprised of a small set of\ncommon blocks. We then optimize and choose the best implementation for each\nblock, determine the combinations of blocks that capture the entire literature,\nand propose novel block combinations. We qualitatively assess the quality of\nthe schemes based on the skyline of efficiency and utility, i.e., based on\nwhether a method is dominated on both aspects or not. Using exhaustive\nexperiments on four real datasets with different characteristics, we conclude\nthat there are always trade-offs in terms of utility and efficiency. We\ndemonstrate that the schemes derived from our novel block combinations provide\nthe best trade-offs for time critical applications. Our work can serve as a\nguide to help practitioners engineer a differentially private histogram scheme\ndepending on their application requirements.\n
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.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".