Query Correlation Attack Against Searchable Symmetric Encryption With Supporting for Conjunctive Queries

IF 8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-27 DOI:10.1109/TIFS.2025.3530692
Hanyong Liu;Lei Xu;Xiaoning Liu;Lin Mei;Chungen Xu
{"title":"Query Correlation Attack Against Searchable Symmetric Encryption With Supporting for Conjunctive Queries","authors":"Hanyong Liu;Lei Xu;Xiaoning Liu;Lin Mei;Chungen Xu","doi":"10.1109/TIFS.2025.3530692","DOIUrl":null,"url":null,"abstract":"Searchable symmetric encryption (SSE) supporting conjunctive queries has garnered significant attention over the past decade due to its practicality and wide applicability. While extensive research has addressed common leakages, such as the access pattern and search pattern, efforts to mitigate these vulnerabilities have primarily focused on structural issues inherent to scheme construction. In this work, we shift the focus to a less explored yet critical leakage stemming from users’ inherent querying behaviors: query correlation. Originally introduced by Grubbs et al. [USENIX SEC’20], formally defined by Oya and Kerschbaum [USENIX SEC’22], and leveraged to mount a high-success query recovery attack against single-keyword SSE, query correlation raises a crucial question: does it pose a similar threat to the security of conjunctive SSE? To tackle this issue, we undertake two key efforts. First, we generalize the notion of query correlation in the context of conjunctive SSE, introducing the “generalized query correlation pattern”, which captures the co-occurrence relationships among queried tokens within a conjunctive query. Second, we develop a new passive query recovery attack, QCCK, which exploits both the search pattern and generalized query correlation pattern to infer the mapping between tokens and keywords. Comprehensive evaluations on the Enron dataset confirm QCCK’s efficacy, achieving a query recovery rate of approximately 80% with a keyword universe size ranging from 200 to 1000 and an observed query size between 5000 and 50,000. These findings highlight the significant threat posed by query correlation in conjunctive SSE and underscore the urgent need for robust countermeasures.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1924-1936"},"PeriodicalIF":8.0000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Information Forensics and Security","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10855610/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Searchable symmetric encryption (SSE) supporting conjunctive queries has garnered significant attention over the past decade due to its practicality and wide applicability. While extensive research has addressed common leakages, such as the access pattern and search pattern, efforts to mitigate these vulnerabilities have primarily focused on structural issues inherent to scheme construction. In this work, we shift the focus to a less explored yet critical leakage stemming from users’ inherent querying behaviors: query correlation. Originally introduced by Grubbs et al. [USENIX SEC’20], formally defined by Oya and Kerschbaum [USENIX SEC’22], and leveraged to mount a high-success query recovery attack against single-keyword SSE, query correlation raises a crucial question: does it pose a similar threat to the security of conjunctive SSE? To tackle this issue, we undertake two key efforts. First, we generalize the notion of query correlation in the context of conjunctive SSE, introducing the “generalized query correlation pattern”, which captures the co-occurrence relationships among queried tokens within a conjunctive query. Second, we develop a new passive query recovery attack, QCCK, which exploits both the search pattern and generalized query correlation pattern to infer the mapping between tokens and keywords. Comprehensive evaluations on the Enron dataset confirm QCCK’s efficacy, achieving a query recovery rate of approximately 80% with a keyword universe size ranging from 200 to 1000 and an observed query size between 5000 and 50,000. These findings highlight the significant threat posed by query correlation in conjunctive SSE and underscore the urgent need for robust countermeasures.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
支持合取查询的可搜索对称加密查询关联攻击
支持连接查询的可搜索对称加密(SSE)由于其实用性和广泛的适用性,在过去十年中引起了广泛的关注。虽然广泛的研究已经解决了常见的泄漏,例如访问模式和搜索模式,但减轻这些漏洞的努力主要集中在方案构建固有的结构问题上。在这项工作中,我们将重点转移到一个较少探索但关键的泄漏,源于用户固有的查询行为:查询相关性。最初由Grubbs等人[USENIX SEC ' 20]提出,由Oya和Kerschbaum [USENIX SEC ' 22]正式定义,并用于对单关键字SSE进行高成功的查询恢复攻击,查询相关性提出了一个关键问题:它是否对连接SSE的安全性构成类似的威胁?为解决这一问题,我们作出了两项关键努力。首先,我们在连词SSE上下文中概括了查询关联的概念,引入了“广义查询关联模式”,该模式捕获了连词查询中查询标记之间的共现关系。其次,我们开发了一种新的被动查询恢复攻击——QCCK,它利用搜索模式和广义查询关联模式来推断令牌和关键字之间的映射关系。对Enron数据集的综合评估证实了QCCK的有效性,在关键字域大小从200到1000,观察到的查询大小在5000到50000之间的情况下,查询恢复率达到了大约80%。这些发现强调了查询相关性在连词SSE中构成的重大威胁,并强调了迫切需要强有力的对策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security 工程技术-工程:电子与电气
CiteScore
14.40
自引率
7.40%
发文量
234
审稿时长
6.5 months
期刊介绍: The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features
期刊最新文献
Mitigating Delivery Fraud and Path Manipulation in UAV-Based E-Commerce: A Fair Exchange Protocol Dishonest Majority Passive-to-Active Compiler over Rings for MPC with Constant Online Communication GCI-GANomaly: A Novel GPS Spoofing Detection Scheme based on Grayscale Constellation Image Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1