Dual Fine-Grained Public-Key Searchable Encryption from Lattices
Bibliographic record
Abstract
Fine-grained public key encryption with keyword search (PEKS), allowing users to search on encrypted data with flexible access control policy, has been widely studied recently due to its promising application to real-world scenarios such as cloud computing. However, most of the existing fine-grained PEKS schemes are either only able to support single access control (e.g., attribute-based access control) or susceptible to being attacked or compromised by quantum computers in or after a short time. In this paper, we propose a fine-grained PEKS scheme that o ers dual access control based on lattice. In particular, we first define a dual fine-grained PEKS primitive against chosen keyword attacks under selective security. Subsequently, we adapt the key homomorphic technique and noise rerandomization technique to design a concrete scheme. Particularly, the keyword space in our construction is unlimited. Then, we present a formal security proof against chosen keyword attacks on the learning with errors (LWE) problem in the standard model. Moreover, we demonstrate the theoretical performance and experimental result of our proposed scheme. Finally, we discuss that our scheme can be easily extended to support conjunctive keywords and delegation without incurring complex operations.
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 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".