The Shadow Nemesis: Inference Attacks on Efficiently Deployable, Efficiently Searchable Encryption

D. Pouliot, C. V. Wright
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引用次数: 92

Abstract

Encrypting Internet communications has been the subject of renewed focus in recent years. In order to add end-to-end encryption to legacy applications without losing the convenience of full-text search, ShadowCrypt and Mimesis Aegis use a new cryptographic technique called "efficiently deployable efficiently searchable encryption" (EDESE) that allows a standard full-text search system to perform searches on encrypted data. Compared to other recent techniques for searching on encrypted data, EDESE schemes leak a great deal of statistical information about the encrypted messages and the keywords they contain. Until now, the practical impact of this leakage has been difficult to quantify. In this paper, we show that the adversary's task of matching plaintext keywords to the opaque cryptographic identifiers used in EDESE can be reduced to the well-known combinatorial optimization problem of weighted graph matching (WGM). Using real email and chat data, we show how off-the-shelf WGM solvers can be used to accurately and efficiently recover hundreds of the most common plaintext keywords from a set of EDESE-encrypted messages. We show how to recover the tags from Bloom filters so that the WGM solver can be used with the set of encrypted messages that utilizes a Bloom filter to encode its search tags. We also show that the attack can be mitigated by carefully configuring Bloom filter parameters.
影子复仇者:对有效部署,有效搜索加密的推理攻击
近年来,对互联网通信进行加密一直是人们重新关注的话题。为了将端到端加密添加到传统应用程序中,同时又不会失去全文搜索的便利性,ShadowCrypt和Mimesis Aegis使用了一种新的加密技术,称为“高效可部署高效可搜索加密”(EDESE),该技术允许标准全文搜索系统对加密数据进行搜索。与最近的其他加密数据搜索技术相比,EDESE方案泄露了大量关于加密消息及其包含的关键字的统计信息。到目前为止,这种泄漏的实际影响一直难以量化。在本文中,我们证明了攻击者将明文关键字与EDESE中使用的不透明密码标识符匹配的任务可以简化为众所周知的加权图匹配(WGM)组合优化问题。使用真实的电子邮件和聊天数据,我们展示了如何使用现成的WGM解决方案来准确有效地从一组edes加密消息中恢复数百个最常见的明文关键字。我们将展示如何从Bloom过滤器中恢复标记,以便WGM求解器可以与使用Bloom过滤器编码其搜索标记的加密消息集一起使用。我们还表明,可以通过仔细配置布隆过滤器参数来减轻攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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