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
We propose a scheme for outsourcing Private Information Retrieval (PIR) to untrusted servers while protecting the privacy of the database owner as well as that of the database clients. We observe that by layering PIR on top of an Oblivious RAM (ORAM) data layout, we provide the ability for the database owner to perform private writes, while database clients can perform private reads from the database even while the owner is offline. Our system is compatible with existing PIR access control and pricing schemes on a per-record basis for these reads. This extends the usual ORAM model by allowing multiple database readers without requiring trusted hardware; indeed, almost all of the computation in our scheme during reads is performed by untrusted cloud servers. We make a second observation that the database owner can always conduct a private read as an ordinary database client, and the private write protocol does not have to provide a "read" functionality as a standard ORAM protocol does. Based on the two observations, we construct an end-to-end system that privately updates a 1 MB record in a 1 TB database with an amortized end-to-end response time as low as 300 ms when the database owner has a fast network connection to the database servers, and about 1 minute over a slow ADSL connection. Private read times by the database readers are on the order of seconds in either case.
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 | 0.000 |
| 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it