Dynamic Searchable Symmetric Encryption with Full Forward Privacy
Bibliographic record
Abstract
Many approaches address the problem of Searchable Symmetric Encryption (SSE), and in the last few years scholars introduced Dynamic SSE (DSSE) schemes in which a client is able to add, delete, and update documents along with performing searches over encrypted documents. The concept of forward privacy was introduced to guarantee a higher level of data privacy and to prevent crucial information leakage. In a forward private scheme, the cloud/attacker cannot link a newly added document to previous searches. However, forward private schemes still leak search pattern which can be employed to collapse the whole security system and an adaptive attacker can reveal plaintext data. To address this challenge, in this paper, we introduce the notion of Full Forward Privacy (FFP). We also propose a parallelizable DSSE scheme that achieves FFP by employing non-deterministic and one-time use search tokens to obfuscate the search pattern. Our cost-efficient scheme supports both updates and searches. Provided security proof and performance analysis demonstrate practicality, efficiency, and security of our approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".