Efficient Private Information Retrieval Protocol with Homomorphically Computing Univariate Polynomials
Bibliographic record
Abstract
Private information retrieval (PIR) protocol is a powerful cryptographic tool and has received considerable attention in recent years as it can not only help users to retrieve the needed data from database servers but also protect them from being known by the servers. Although many PIR protocols have been proposed, it remains an open problem to design an efficient PIR protocol whose communication overhead is irrelevant to the database size N . In this paper, to answer this open problem, we present a new communication-efficient PIR protocol based on our proposed single-ciphertext fully homomorphic encryption (FHE) scheme, which supports unlimited computations with single variable over a single ciphertext even without access to the secret key. Specifically, our proposed PIR protocol is characterized by combining our single-ciphertext FHE with Lagrange interpolating polynomial technique to achieve better communication efficiency. Security analyses show that the proposed PIR protocol can efficiently protect the privacy of the user and the data in the database. In addition, both theoretical analyses and experimental evaluations are conducted, and the results indicate that our proposed PIR protocol is also more efficient and practical than previously reported ones. To the best of our knowledge, our proposed protocol is the first PIR protocol achieving O1 communication efficiency on the user side, irrelevant to the database size N .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".