基于频率分析的加密重复数据删除中的信息泄露

Jingwei Li, P. Lee, Chufeng Tan, Chuan Qin, Xiaosong Zhang
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引用次数: 31

摘要

加密重复数据删除将加密和重复数据删除相结合,在保证数据安全性的同时提高存储效率。目前最先进的加密重复数据删除系统主要建立在确定性加密的基础上,以保持重复数据删除的有效性。然而,这种确定性加密揭示了原始明文块的底层频率分布。这允许攻击者对密文块进行频率分析,并推断原始明文块的内容。在本文中,我们从攻击和防御两个角度研究频率分析如何影响加密重复数据删除中的信息泄漏。具体来说,我们针对备份工作负载,提出了一种新的推理攻击,利用块局部性来增加推断块的覆盖范围。我们进一步将新的推理攻击与块大小的知识结合起来,并展示了它对变大小块的攻击有效性。我们对真实世界和合成数据集进行了跟踪驱动的评估,并表明我们提出的攻击推断出备份工作负载下的明文块的很大一部分。为了防御频率分析,我们提出了两种防御方法,即MinHash加密和置乱。我们的跟踪驱动评估表明,我们的组合MinHash加密和置乱方案有效地减轻了推理攻击的严重性,同时保持了高存储效率和有限的元数据访问开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information Leakage in Encrypted Deduplication via Frequency Analysis
Encrypted deduplication combines encryption and deduplication to simultaneously achieve both data security and storage efficiency. State-of-the-art encrypted deduplication systems mainly build on deterministic encryption to preserve deduplication effectiveness. However, such deterministic encryption reveals the underlying frequency distribution of the original plaintext chunks. This allows an adversary to launch frequency analysis against the ciphertext chunks and infer the content of the original plaintext chunks. In this article, we study how frequency analysis affects information leakage in encrypted deduplication, from both attack and defense perspectives. Specifically, we target backup workloads and propose a new inference attack that exploits chunk locality to increase the coverage of inferred chunks. We further combine the new inference attack with the knowledge of chunk sizes and show its attack effectiveness against variable-size chunks. We conduct trace-driven evaluation on both real-world and synthetic datasets and show that our proposed attacks infer a significant fraction of plaintext chunks under backup workloads. To defend against frequency analysis, we present two defense approaches, namely MinHash encryption and scrambling. Our trace-driven evaluation shows that our combined MinHash encryption and scrambling scheme effectively mitigates the severity of the inference attacks, while maintaining high storage efficiency and incurring limited metadata access overhead.
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