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Effective query recovery attacks against Searchable Symmetric Encryption(SSE) schemes typically rely on auxiliary ground-truth information about thequeries or dataset. Query recovery is also possible under the weakerstatistical auxiliary information assumption, although statistical-basedattacks achieve lower accuracy and are not considered a serious threat. In thiswork we present IHOP, a statistical-based query recovery attack that formulatesquery recovery as a quadratic optimization problem and reaches a solution byiterating over linear assignment problems. We perform an extensive evaluationwith five real datasets, and show that IHOP outperforms all otherstatistical-based query recovery attacks under different parameter and leakageconfigurations, including the case where the client uses some access-patternobfuscation defenses. In some cases, our attack achieves almost perfect queryrecovery accuracy. Finally, we use IHOP in a frequency-only leakage settingwhere the client's queries are correlated, and show that our attack can exploitquery dependencies even when PANCAKE, a recent frequency-hiding defense byGrubbs et al., is applied. Our findings indicate that statistical queryrecovery attacks pose a severe threat to privacy-preserving SSE schemes.

IHOP: Improved Statistical Query Recovery against Searchable Symmetric Encryption through Quadratic Optimization