Two-Party Computation Model for Privacy-Preserving Queries over Distributed Databases.
Why this work is in the frame
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Bibliographic record
Abstract
Many existing privacy-preserving techniques for querying distributed databases of sensitive information do not scale for large databases due to the use of heavyweight cryptographic techniques. In addition, many of these protocols require several rounds of interactions between the participants which may be impractical in wide-area settings. At the other extreme, a trusted party based approach does provide scalability but it forces the individual databases to reveal private information to the central party. This paper shows how to perform various privacypreserving operations in a scalable manner under the honest-but-curious model. Our system provides the same level of scalability as a trusted central party based solution while providing privacy guarantees without the need for heavyweight cryptography. The key idea is to develop an alternative system model using a Two-Party Query Computation Model comprising of a randomizer and a computing engine which do not reveal any information between themselves. We also show how one can replace the randomizer by a lightweight key-agreement protocol. We formally prove the privacy-preserving properties of our protocols and demonstrate the scalability and practicality of our system using a real-world implementation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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