Two-Server Offline/Online Private Information Retrieval With Small Client Storage
Bibliographic record
Abstract
In this paper, we propose PIRS, a two-server offline/online private information retrieval scheme with small client storage. In PIRS, a client first engages in an offline phase to preprocess a database replicated on two servers to generate query-independent hints. Utilizing the pre-computed hints, the client then securely retrieves any record from the database without exposing its index during an online phase, with the server-side computational complexity being sublinear for high efficiency. Compared to state-of-the-art schemes, PIRS distinguishes itself by enabling the client to outsource the hints to the servers instead of storing them, dramatically reducing the local storage requirement from GB/MB to MB/KB, given that the size of the hints is directly proportional to the database volume. Specifically, the client employs secret sharing to achieve secure hint outsourcing and only fetches the relevant hint for each online PIR query. In such an outsourcing environment, we introduce a new technique named oblivious switching to obfuscate repeated hint/record accesses, and carefully tailor online PIR queries to guarantee sublinear computational complexity. Furthermore, we propose a secure and efficient method that delegates the task of locating appropriate hints for particular PIR queries to the servers, thus avoiding costly computations or extra data storage on the client side. Finally, we conduct a comprehensive security analysis to demonstrate PIRS's security, and develop a proof-of-concept prototype to show the practicality of PIRS in terms of computational, communication, and storage overheads.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".