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Hardening Database Padding for Searchable Encryption

机译:强化数据库填充以进行可搜索的加密

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Searchable encryption (SE) is a practical crypto-graphic primitive to build encrypted databases. Recently there has been much attention in leakage-abuse attacks against SE. Among others, attacks based on inference of keyword frequency can easily identify query keywords from the access pattern, i.e., query results. To mitigate these attacks, database padding is considered as a conceptually simple yet effective counter-measure. Unfortunately, none of the existing studies formally understand the relationship between padding security strength and its overhead. Also, how to craft padding is not restricted in current countermeasures, where bogus files are likely to be distinguishable from real ones. In this paper, we propose an information theory based framework to analyse the security strength under certain padding overhead. First, we leverage relative entropy to measure the “closeness” between the distributions of the original dataset and padded dataset. Second, we quantity the attack efforts against padding countermeasures by entropy analysis. Apart from theoretical findings, we further devise an algorithm via outlier detection for padding generation, which considers both the padded dataset distribution and the similarity between real and bogus files. Evaluations on a real-world dataset confirm our theoretical results and demonstrate the efficiency and effectiveness of our proposed padding generation algorithm.
机译:可搜索加密(SE)是一种实用的加密图形原语,用于建立加密的数据库。最近,针对SE的泄漏滥用攻击引起了很多关注。其中,基于关键词频率的推断的攻击可以容易地从访问模式即查询结果中识别出查询关键词。为了减轻这些攻击,数据库填充被认为是概念上简单而有效的对策。不幸的是,现有的研究都没有正式了解填充安全强度与其开销之间的关系。另外,在当前的对策中,如何伪造填充不受限制,因为伪造文件很可能与真实文件区分开。在本文中,我们提出了一个基于信息论的框架来分析某些填充开销下的安全强度。首先,我们利用相对熵来度量原始数据集和填充数据集的分布之间的“接近度”。其次,我们通过熵分析来量化针对填充对策的攻击力度。除了理论上的发现外,我们还通过离群检测进一步设计了一种用于填充生成的算法,该算法同时考虑了填充数据集的分布以及真实文件与虚假文件之间的相似性。对真实数据集的评估证实了我们的理论结果,并证明了我们提出的填充生成算法的效率和有效性。

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