Private Information Retrieval from Locally Repairable Databases with Colluding Servers
Bibliographic record
Abstract
Information-theoretical private information retrieval (PIR) is considered from a coded database with colluding servers. The storage code is a locally repairable code (LRC) with maximal recoverability (MR), and in particular, with optimal global minimum distance, for arbitrary code parameters: Number of local groups g, locality r, local distance δ, dimension k ≤ gr and length n = g(r + δ - 1). Servers are identified bijectively with local groups, and only locally non-redundant information is considered and downloaded from each server, that is, only r nodes (out of r + δ - 1) are considered per server. When the remaining MDS code, after removing all locally redundant nodes, is a linearized Reed-Solomon code, a PIR scheme is provided achieving the (download) rate R = (N - k - rt + 1)/N, where N = gr = n - g(δ - 1) is the length of the restricted MDS code, for any t colluding servers such that k + rt ≤ N. The field size is roughly gr, polynomial in the number of servers g. Assume an arbitrarily large number of stored files. If N - k - rt = 0, the rate R = 1/N is the highest known and coincides with that of previous PIR schemes that work for any MDS storage code. If N - k - rt > 0, the achieved rate R > 1/N coincides with the best known rate of PIR schemes for MDS storage codes (but which do not work for LRCs or linearized Reed-Solomon storage codes) and is always strictly higher than that of known PIR schemes that work for arbitrary MDS storage codes.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".