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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".