Revisiting the attacker's knowledge in inference attacks against Searchable Symmetric Encryption
This work investigates the dependence of inference attacks in Searchable Symmetric Encryption (SSE) on the quality of “similar data” available to the adversary. We propose the first general statistical analysis framework that formally defines “similar data” and reveals how its non-uniqueness critically impacts attack robustness. We prove that index size constraints significantly degrade inference attack efficacy and derive a provably secure lower bound on the required index size. Within the leakage-abuse model, we integrate probabilistic modeling with statistical estimation theory and empirically validate our findings on the Enron dataset: imposing an index size cap of 200 reduces the optimal inference attack’s accuracy to below 5% with high probability. Our results yield the first quantifiable, data-similarity-aware defense configuration guideline for SSE systems—bridging theoretical security guarantees with practical deployment constraints.