Bibliographic record
Abstract
We extend Goldberg's multi-server information-theoretic private information retrieval (PIR) with a suite of protocols for privacy-preserving e-commerce. Our first protocol adds support for single-payee tiered pricing, wherein users purchase database records without revealing the indices or prices of those records. Tiered pricing lets the seller set prices based on each user's status within the system; e.g., non-members may pay full price while members may receive a discounted rate. We then extend tiered pricing to support group-based access control lists with record-level granularity; this allows the servers to set access rights based on users' price tiers. Next, we show how to do some basic bookkeeping to implement a novel top-K replication strategy that enables the servers to construct bestsellers lists, which facilitate faster retrieval for these most popular records. Finally, we build on our bookkeeping functionality to support multiple payees, thus enabling several sellers to offer their digital goods through a common database while enabling the database servers to determine to what portion of revenues each seller is entitled. Our protocols maintain user anonymity in addition to query privacy; that is, queries do not leak information about the index or price of the record a user purchases, the price tier according to which the user pays, the user's remaining balance, or even whether the user has ever queried the database before. No other priced PIR or oblivious transfer protocol supports tiered pricing, access control lists, multiple payees, or top-K replication, whereas ours supports all of these features while preserving PIR's sublinear communication complexity. We have implemented our protocols as an add-on to Percy++, an open source implementation of Goldberg's PIR scheme. Measurements indicate that our protocols are practical for deployment in real-world e-commerce applications.
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.
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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".