An Efficient, Verifiable, and Dynamic Searchable Symmetric Encryption with Forward Privacy
Bibliographic record
Abstract
Dynamic Searchable Symmetric Encryption (DSSE) allows a cloud server to perform search queries on a user’s documents while both queries and files are encrypted. It also enables the user to update the corpus efficiently. Recently, the notion of Forward Privacy (FP) was introduced, which guarantees the privacy of a newly added document in the presence of previous queries. However, most of the existing approaches work only with honest-but-curious servers. In these schemes, it is assumed the cloud server follows the prescribed protocols, but due to the untrusted nature of the cloud servers, this assumption does not always hold in practice. Hence, it is essential to design and implement new approaches that verify the results of queries and detect malicious behavior of a cloud server. In this paper, we construct a new forward-private DSSE scheme that efficiently achieves result verifiability. To obtain this goal, along with the search results, the server provides a "proof of work" to demonstrate result completeness. Moreover, our approach support searches and updates efficiently. Finally, the security and practicality of our scheme is demonstrated by providing performance analysis and security proof.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".