使用可信执行的实用卷隐藏范围可搜索对称加密

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Xu Yang, Ke Li, Saiyu Qi, Hongguang Zhao
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引用次数: 0

摘要

可搜索对称加密(SSE)使客户能够安全地将私有数据外包给云服务器,同时保留搜索功能。范围查询涉及到将文档与具有一系列关键字的字段进行匹配,解决对范围查询日益增长的需求仍然是一个挑战。现有的卷隐藏范围查询方案无法解决文档获取阶段的卷模式泄漏问题,并且缺乏对文档添加和删除等动态操作的支持。为了克服这些限制,我们提出了EDVHRQ,这是一种高效、动态、卷隐藏的范围查询方案,由Intel SGX支持。我们的方案引入了新的策略:(1)分组,它隐藏了响应大小,同时最小化了服务器存储和通信成本;(2)最优最佳范围覆盖(OBRC)方法,它将查询范围转换为最小的陷阱门集,以加速范围查询。与现有的解决方案不同,EDVHRQ在客户端和云服务器之间实现了一个单一的往返查询过程。我们正式分析了EDVHRQ的安全保证,证明了它对体积模式泄漏的鲁棒性。实验评估突出了其优越的性能,与HybrIDX和SEAL相比,查询执行速度分别提高了18.8倍和56.5倍,同时显著降低了服务器存储成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Practical volume-hiding range searchable symmetric encryption using trusted execution
Searchable symmetric encryption (SSE) enables clients to securely outsource private data to cloud servers while preserving search functionality. Addressing the increasing demand for range queries, which involve matching documents to fields with a range of keywords, remains a challenge. Existing volume-hiding range query schemes fail to address volume pattern leakage during the document fetch phase and lack support for dynamic operations such as addition and deletion of documents. To overcome these limitations, we propose EDVHRQ, an efficient, dynamic, and volume-hiding range query scheme enabled by Intel SGX. Our scheme introduces novel strategies: (1) packetization, which conceals response sizes while minimizing server storage and communication costs, and (2) the optimal best range cover (OBRC) method, which transforms query ranges into a minimal set of trapdoors to accelerate range queries. Unlike existing solutions, EDVHRQ achieves a single roundtrip query process between the client and the cloud server. We formally analyze the security guarantees of EDVHRQ, demonstrating its robustness against volume pattern leakage. Experimental evaluations highlight its superior performance, achieving up to 18.8× and 56.5× faster query execution compared to HybrIDX and SEAL, respectively, while significantly reducing server storage costs.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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