Paper 2024/516
Similar Data is Powerful: Enhancing Inference Attacks on SSE with Volume Leakages
Abstract
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.
Note: This paper is currently accepted by ESORICS 2024.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint.
- Keywords
- Searchable Symmetric EncryptionLeakageInference AttackVolume Pattern
- Contact author(s)
-
b ho @ studet tudelft nl
h chen-2 @ tudelft nl
z shi-2 @ tudelft nl
kaitai liang @ tudelft nl - History
- 2024-04-15: revised
- 2024-04-01: received
- See all versions
- Short URL
- https://ia.cr/2024/516
- License
-
CC BY-NC
BibTeX
@misc{cryptoeprint:2024/516,
author = {Björn Ho and Huanhuan Chen and Zeshun Shi and Kaitai Liang},
title = {Similar Data is Powerful: Enhancing Inference Attacks on SSE with Volume Leakages},
howpublished = {Cryptology ePrint Archive, Paper 2024/516},
year = {2024},
note = {\url{https://eprint.iacr.org/2024/516}},
url = {https://eprint.iacr.org/2024/516}
}
