Parity-based inference control for multi-dimensional range sum queries
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Bibliographic record
Abstract
This paper studies the inference control of multi-dimensional range (MDR) sum queries. We show that existing inference control methods are usually inefficient for MDR queries. We then consider parity-based inference control that restricts users to queries involving an even number of sensitive values. Such a restriction renders inferences significantly more difficult, because an even number is closed under addition and subtraction, whereas inferences target at one value. However, more sophisticated inferences are still possible with only even MDR queries. We show that the collection of all even MDR queries causes inferences if and only if a special collection of sum-two queries (that is, the summation of exactly two values) does so. The result leads to an inference control method with an improved computational complexity [Formula: see text] (over the previous result of [Formula: see text]) for m MDR queries over n values. We show that no odd MDR queries can be answered without causing inferences. We show how to check non-MDR queries for inferences in linear time. We also show how to find large inference-free subsets of even MDR queries when they do cause inferences.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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