Hiding the Access Pattern is Not Enough: Exploiting Search Pattern\n Leakage in Searchable Encryption
Bibliographic record
Abstract
Recent Searchable Symmetric Encryption (SSE) schemes enable secure searching\nover an encrypted database stored in a server while limiting the information\nleaked to the server. These schemes focus on hiding the access pattern, which\nrefers to the set of documents that match the client's queries. This provides\nprotection against current attacks that largely depend on this leakage to\nsucceed. However, most SSE constructions also leak whether or not two queries\naim for the same keyword, also called the search pattern.\n In this work, we show that search pattern leakage can severely undermine\ncurrent SSE defenses. We propose an attack that leverages both access and\nsearch pattern leakage, as well as some background and query distribution\ninformation, to recover the keywords of the queries performed by the client.\nOur attack follows a maximum likelihood estimation approach, and is easy to\nadapt against SSE defenses that obfuscate the access pattern. We empirically\nshow that our attack is efficient, it outperforms other proposed attacks, and\nit completely thwarts two out of the three defenses we evaluate it against,\neven when these defenses are set to high privacy regimes. These findings\nhighlight that hiding the search pattern, a feature that most constructions are\nlacking, is key towards providing practical privacy guarantees in SSE.\n
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".