Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2022
Abstract
Searchable Encryption schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access and volume pattern. In most existing SE schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Recent attacks have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Under the umbrella of attacking SE, we design a new Volume and Access Pattern Leakage-Abuse Attack (VAL-Attack) that improves the matching technique of LEAP (CCS ’21) and exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs without false positives. We further compare VAL-Attack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures.
Keywords
Searchable encryption, Access pattern, Volume pattern, Leakage, Attack
Discipline
Information Security
Publication
Proceedings of the 27th European Symposium on Research in Computer Security, Copenhagen, Denmark, 2022 September 26-30
Volume
13554 LNCS
First Page
653
Last Page
676
ISBN
9783031171390
Identifier
10.1007/978-3-031-17140-6_32
Publisher
Springer
City or Country
Cham
Citation
LAMBREGTS, Steven; CHEN, Huanhuan; NING, Jianting; and LIANG, Kaitai.
Val: Volume and access pattern leakage-abuse attack with leaked documents. (2022). Proceedings of the 27th European Symposium on Research in Computer Security, Copenhagen, Denmark, 2022 September 26-30. 13554 LNCS, 653-676.
Available at: https://ink.library.smu.edu.sg/sis_research/10193
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-031-17140-6_32