On the Meaning and Limits of Empirical Differential Privacy
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
Empirical differential privacy (EDP) has been proposed as an alternative to differential privacy (DP), with the important advantages that the procedure can be applied to any bayesian model and requires less technical work from the part of the user. While EDP has been shown to be easy to implement, little is known of its theoretical underpinnings. This paper proposes a careful investigation of the meaning and limits of EDP as a measure of privacy. We show that EDP can not simply be considered an empirical version of DP, and that it could instead be thought of as a sensitivity measure on posterior distributions. We also show that EDP is not well-defined, in that its value depends crucially on the choice of discretization used in the procedure, and that it can be very computationnaly intensive to apply in practice. We illustrate these limitations with two simple conjugate bayesian model: the beta-binomial model and the normal-normal model.
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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 | 0.001 | 0.020 |
| 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 | 0.001 | 0.001 |
| Open science | 0.015 | 0.025 |
| 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