Verifiable symmetric searchable encryption for semi-honest-but-curious cloud servers
Bibliographic record
Abstract
Outsourcing data to cloud servers, while increasing service availability and reducing users' burden of managing data, inevitably brings in new concerns such as data privacy, since the server may be honest-but-curious. To mediate the conflicts between data usability and data privacy in such a scenario, research of searchable encryption is of increasing interest. Motivated by the fact that a cloud server, besides its curiosity, may be selfish in order to save its computation and/or download bandwidth, in this paper, we investigate the searchable encryption problem in the presence of a semi-honest-but-curious server, which may execute only a fraction of search operations honestly and return a fraction of search outcome honestly. To fight against this strongest adversary ever, a verifiable SSE (VSSE) scheme is proposed to offer verifiable searchability in additional to the data privacy, both of which are further confirmed by our rigorous security analysis. Besides, we treat the practicality/efficiency as a central requirement of a searchable encryption scheme. To demonstrate the lightweightness of our scheme, we implemented and tested the proposed VSSE on a laptop (serving as the server) and a mobile phone running Android 2.3.4 (serving as the end user). The experimental results optimistically suggest that the proposed scheme satisfies all of our design goals.
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 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".