Achieving Practical Symmetric Searchable Encryption With Search Pattern Privacy Over Cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dynamic symmetric searchable encryption (SSE), which enables a data user to securely search and dynamically update the encrypted documents stored in a semi-trusted cloud server, has received considerable attention in recent years. However, the search and update operations in many previously reported SSE schemes will bring some additional privacy leakages, e.g., search pattern privacy, forward privacy and backward privacy. To the best of our knowledge, none of the existing dynamic SSE schemes preserves the search pattern privacy, and many backward private SSE schemes still leak some critical information, e.g., the identifiers containing a specific keyword currently in the database. Therefore, aiming at the above challenges, in this article, we design a practical SSE scheme, which not only supports the search pattern privacy but also enhances the backward privacy. Specifically, we first leverage the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity and encryption to design an obfuscating technique. Then, based on the obfuscating technique, pseudorandom function and pseudorandom generator, we design a basic dynamic SSE scheme to support single keyword queries and simultaneously achieve search pattern privacy and enhanced backward privacy. Furthermore, we also extend our proposed scheme to support more efficient boolean queries. Security analysis demonstrates that our proposed scheme can achieve the desired privacy properties, and the extensive performance evaluations also show that our proposed scheme is indeed efficient in terms of communication overhead and computational cost.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it