Leakage-abuse Attacks Against Forward Private Searchable Symmetric Encryption
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 Searchable Symmetric Encryption (DSSE) methods address the problem of securely outsourcing\updating private data into a semi-trusted cloud server. Furthermore, Forward Privacy (FP) notion was introduced to limit data leakage and thwart the related attacks on DSSE approaches. FP schemes ensure previous search queries cannot be linked to future updates and newly added files. Since FP schemes use ephemeral search tokens and one-time use index entries, many scholars conclude that privacy attacks on traditional SSE schemes do not apply to SSE approaches that support forward privacy. However, to obtain efficiency, all FP approaches accept a certain level of data leakage, including access pattern leakage. Here, we introduce two new attacks on forward-private schemes. We demonstrate that it is still plausible to accurately unveil the search pattern by reversing the access pattern. Afterward, the attackers can exploit this information to uncover the search queries and consequently the documents. We also show that the traditional privacy attacks on SSE schemes are still applicable to schemes that support forward privacy. We then construct a new DSSE approach that supports parallelism and obfuscates the search and access pattern to thwart the introduced attacks. Our scheme is cost-efficient and provides secure search and update. Our performance analysis and security proof demonstrate our approach's practicality, efficiency, and security.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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