One (Block) Size Fits All: PIR and SPIR with Variable-Length Records via Multi-Block Queries.
Bibliographic record
Abstract
We propose a new, communication-efficient way for users to fetch multiple blocks simultaneously in Goldberg’s robust information-theoretic private information retrieval (IT-PIR) scheme. Our new multi-block IT-PIR trades off some Byzantine robustness to improve throughput without affecting user privacy. By taking advantage of the recent Cohn-Heninger multi-polynomial list decoding algorithm, we show how realistic parameter choices enable the user to retrieve several blocks without increasing the commu-nication or computation costs beyond what is required to retrieve a single block, and argue that the resulting scheme still maintains essentially optimal Byzantine robustness in practice. We also derive optimal parameters for our construction, which yields communication costs within a small factor of the lowest possible. With our new multi-block IT-PIR protocol as a starting point, we construct four new symmetric PIR (SPIR) protocols that each support variable-length database records. By decoupling the PIR block size from the lengths of individual database records, we are free to fix the block size to its communication-optimal value without artificially restricting the contents and layout of the records. Moreover, it is straightforward to augment three of our four new SPIR constructions with efficient zero-knowledge proofs about the particular records a user is requesting in a given query; this makes it easy to implement pricing and access control structures over the records using standard techniques from the literature. The resulting SPIR protocols are therefore well suited to privacy-preserving e-commerce applications, such as privacy-friendly sales of e-books, music, movies, or smart phone and tablet apps.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".