Similar Data is Powerful: Enhancing Inference Attacks on SSE with Volume Leakages

Searchable symmetric encryption (SSE) schemes provide users with the ability to perform keyword searches on encrypted databases without the need for decryption. While this functionality is advantageous, it introduces the potential for inadvertent information disclosure, thereby exposing SSE systems to various types of attacks. In this work, we introduce a new inference attack aimed at enhancing the query recovery accuracy of RefScore (presented at USENIX 2021). The proposed approach capitalizes on both similar data knowledge and an additional volume leakage as auxiliary information, facilitating the extraction of keyword matches from leaked data. Empirical evaluations conducted on multiple real-world datasets demonstrate a notable enhancement in query recovery accuracy, up to 19.5\%. We also analyze the performance of the proposed attack in the presence of diverse countermeasures.