Keyword Search over Shared Cloud Data without Secure Channel or Authority
Bibliographic record
Abstract
Storage services play an important role in a public cloud. By outsourcing data to the remote cloud, users do not need to maintain a local storage infrastructure and can significantly lower the storage cost. To protect the privacy, documents must be encrypted before outsourcing. This raises a new challenge for the document owner: how should the encrypted documents be securely searched in a public cloud? While many mechanisms have been proposed to support secure search over the encrypted documents, most of these mechanisms require secure channels to transmit the secret information, such as the secret keys and trapdoors, and is difficult to deploy in cloud systems. Moreover, some existing mechanisms require an authority to control the access requests of users, which inevitably increases the complexity of cloud infrastructure. This paper considers a more stringent security model where an eavesdropper exists in the cloud and can eavesdrop on all transmission channels. We propose a novel mechanism that supports multi-user keyword search over the encrypted data without relying on any secure channel or authority. The eavesdropper can neither forge valid trapdoors from the intercepted information nor can it directly use the intercepted trapdoors to complete the keyword search. Security analysis shows that the proposed mechanism is secure.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".