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
A private data federation enables clients to query the union of data from multiple data providers without revealing any extra private information to the client or any other data providers. Unfortunately, this strong end-to-end privacy guarantee requires cryptographic protocols that incur a significant performance overhead as high as 1,000 x compared to executing the same query in the clear. As a result, private data federations are impractical for common database workloads. This gap reveals the following key challenge in a private data federation: offering significantly fast and accurate query answers without compromising strong end-to-end privacy. To address this challenge, we propose SAQE, the Secure Approximate Query Evaluator, a private data federation system that scales to very large datasets by combining three techniques --- differential privacy, secure computation, and approximate query processing --- in a novel and principled way. First, SAQE adds novel secure sampling algorithms into the federation's query processing pipeline to speed up query workloads and to minimize the noise the system must inject into the query results to protect the privacy of the data. Second, we introduce a query planner that jointly optimizes the noise introduced by differential privacy with the sampling rates and resulting error bounds owing to approximate query processing. Our research shows that these three techniques are synergistic: sampling within certain accuracy bounds improves both query privacy and performance, meaning that SAQE executes over less data than existing techniques without sacrificing efficiency, privacy, or accuracy. Using our optimizer, we leverage this counter-intuitive result to identify an inflection point that maximizes all three criteria prior query evaluation. Experimentally, we show that this result enables SAQE to trade-off among these three criteria to scale its query processing to very large datasets with accuracy bounds dependent only on sample size, and not the raw data size.
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.
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.030 | 0.082 |
| 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