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Cloud data storage solutions offer customers cost-effective and reduced datamanagement. While attractive, data security issues remain to be a core concern.Traditional encryption protects stored documents, but hinders simplefunctionalities such as keyword search. Therefore, searchable encryptionschemes have been proposed to allow for the search on encrypted data. Efficientschemes leak at least the access pattern (the accessed documents per keywordsearch), which is known to be exploitable in query recovery attacks assumingthe attacker has a significant amount of background knowledge on the storeddocuments. Existing attacks can only achieve decent results with strongadversary models (e.g. at least 20% of previously known documents or requireadditional knowledge such as on query frequencies) and they give no metric toevaluate the certainty of recovered queries. This hampers their practicalutility and questions their relevance in the real-world.  We propose a refined score attack which achieves query recovery rates ofaround 85% without requiring exact background knowledge on stored documents; adistributionally similar, but otherwise different (i.e., non-indexed), datasetsuffices. The attack starts with very few known queries (around 10 knownqueries in our experiments over different datasets of varying size) and theniteratively recovers further queries with confidence scores by addingpreviously recovered queries that had high confidence scores to the set ofknown queries. Additional to high recovery rates, our approach yieldsinterpretable results in terms of confidence scores.

A Highly Accurate Query-Recovery Attack against Searchable Encryption using Non-Indexed Documents