Query Recovery Attack Based on Multi-Source Leakage and Semantic Embedding in Searchable Symmetric Encryption

With the rapid development of cloud computing and big data technologies, searchable encryption has become a research hotspot. To improve search efficiency, searchable encryption algorithms may leak redundant information, allowing adversaries to launch query recovery attacks by exploiting pattern leakage in searchable symmetric encryption and infer the underlying keywords of user queries. Existing query recovery attack methods only perform query recovery under multi-source leakage patterns, without considering the semantic level; they simulate user queries with plaintext rather than ciphertext and rarely take frequency as auxiliary leakage for attacks. Meanwhile, the weights between volume and frequency mostly rely on manual configuration, without dynamic allocation of the weights for volume and frequency. Therefore, this study proposes a novel attack method named refine atk. First, a multi-layer perceptron is used to learn the weights of volume and frequency to accurately identify and recover distinctive queries. Next, co-occurrence information is employed to correct the queries recovered in the previous step. Finally, a cost matrix is constructed using the weighted co-occurrence matrix and the semantic embedding matrix obtained by the pre-trained language model MiniLM-L6, and the remaining queries are recovered in one pass via greedy graph matching. The proposed attack achieves an attack accuracy of 95% on the Enron and Lucene datasets. The attack performance remains robust even after removing part of the similar data. The attack execution efficiency of the proposed method is significantly superior to that of the traditional schemes, yielding better performance when attacking concrete searchable symmetric encryption schemes or evaluating their security. This work provides a reference for the security evaluation and defense mechanism design of searchable symmetric encryption.

@article{Yuan2026,
  title     = {Query Recovery Attack Based on Multi-Source Leakage and Semantic Embedding in Searchable Symmetric Encryption},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Xiaogang Yuan and Xinle Yang and Dezhi An},
  doi       = {10.14569/IJACSA.2026.0170676},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170676}
}