[论文解读] A Highly Accurate Query-Recovery Attack against Searchable Encryption using Non-Indexed Documents

Marc Damie, Florian Hahn+1 more|University of Twente Research Information|Jun 27, 2023

Cryptography and Data Security参考文献 16被引用 4

一句话总结

本文提出了一种针对可搜索对称加密（SSE）的高效相似数据查询恢复攻击，仅使用10个已知查询和分布相似的非索引文档，即可实现高达90%的查询恢复准确率。该攻击采用优化的置信度评分机制，以高可解释性与极少的敌手先验知识，迭代恢复关键词，其性能优于以往需要精确掌握20%以上索引文档的攻击方法。

ABSTRACT

Cloud data storage solutions offer customers cost-effective and reduced data management. While attractive, data security issues remain to be a core concern. Traditional encryption protects stored documents, but hinders simple functionalities such as keyword search. Therefore, searchable encryption schemes have been proposed to allow for the search on encrypted data. Efficient schemes leak at least the access pattern (the accessed documents per keyword search), which is known to be exploitable in query recovery attacks assuming the attacker has a significant amount of background knowledge on the stored documents. Existing attacks can only achieve decent results with strong adversary models (e.g. at least 20% of previously known documents or require additional knowledge such as on query frequencies) and they give no metric to evaluate the certainty of recovered queries. This hampers their practical utility and questions their relevance in the real-world. We propose a refined score attack which achieves query recovery rates of around 85% without requiring exact background knowledge on stored documents; a distributionally similar, but otherwise different (i.e., non-indexed), dataset suffices. The attack starts with very few known queries (around 10 known queries in our experiments over different datasets of varying size) and then iteratively recovers further queries with confidence scores by adding previously recovered queries that had high confidence scores to the set of known queries. Additional to high recovery rates, our approach yields interpretable results in terms of confidence scores.

研究动机与目标

• 开发一种实用的被动查询恢复攻击，针对SSE方案，且无需了解实际的索引文档内容。
• 克服以往攻击方法依赖精确掌握20%或以上索引文档的局限性。
• 仅利用分布相似的非索引文档（如先前数据泄露中的文档）实现有效的查询恢复。
• 提出一种置信度评分指标，为恢复的查询提供可解释且高精度的结果。
• 评估查询分布与已知查询选择对攻击效果的影响。

提出的方法

• 攻击使用优化的评分函数，评估给定查询标记（陷门）与特定关键词匹配的可能性，当配对正确时使得分最大化。
• 从少量已知查询-标记对（最少10对）开始，通过迭代将高置信度恢复的配对加入已知集合，逐步扩展已知集。
• 置信度评分基于查询结果集与已知文档之间的重叠程度计算，利用分布相似性。
• 该方法假设攻击者可访问与加密文档分布相似但不属于索引的文档数据集。
• 采用统计建模估算陷门-关键词匹配的可能性，评分经校准以反映恢复置信度。
• 在均匀分布与齐夫分布（Zipfian）查询分布下评估攻击，以评估其在现实场景中的适用性。

Figure 2: Average accuracy of the base matching algorithm on Enron for vocabulary size. Parameters: $|\mathcal{D}_{\text{sim}}|=12K,|\mathcal{D}_{\text{real}}|=18K,|\mathcal{Q}|=0.15\cdot m_{\text{real}}$

实验结果

研究问题

• RQ1 被动查询恢复攻击是否能在不精确掌握索引文档的情况下实现高准确率？
• RQ2 当攻击者仅能访问分布相似的非索引文档时，攻击性能如何变化？
• RQ3 已知查询的分布（如高频词与随机关键词）在多大程度上影响攻击的成功率？
• RQ4 与通常假设的均匀分布相比，真实世界的查询分布（如齐夫分布）在攻击效果上表现如何？
• RQ5 置信度评分能否为查询恢复攻击提供可解释且可靠的结果？

主要发现

• 仅使用10个已知查询标记和非索引的分布相似文档，优化评分攻击的查询恢复率最高可达90%。
• 在齐夫分布查询下，当词汇量为1,000时，攻击准确率达到91%，显著优于均匀分布下的表现。
• 随着已知查询数量增加，攻击性能提升，且当使用高频关键词作为已知查询时，准确率的方差减小。
• 在非均匀查询分布（如齐夫分布）下，攻击更为有效，与均匀假设相比，恢复准确率几乎翻倍。
• 置信度评分指标提供可解释的结果，使攻击者能够评估每个恢复查询的可靠性。
• 现有防护措施（如填充和混淆）被证明能有效缓解优化评分攻击。

Figure 3: Average execution time of the matching algorithm for vocabulary size Parameters: $|\mathcal{D}_{\text{sim}}|=12K,|\mathcal{D}_{\text{real}}|=18K,|\mathcal{Q}|=0.15\cdot m_{\text{real}},|\text{Known}\mathcal{Q}|=10$