Leakage-abuse Attacks Against Forward Private Searchable Symmetric Encryption
Bibliographic record
Abstract
Access pattern leakage Search pattern leakage Data privacy Leakage-abuse attacksDynamic 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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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.006 |
| 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".