垂直分区数据库上的外包隐私保护数据对齐

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhuzhu Wang;Cui Hu;Bin Xiao;Yang Liu;Teng Li;Zhuo Ma;Jianfeng Ma
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引用次数: 0

摘要

在现实世界安全外包计算的背景下,私有数据对齐一直是必不可少的预处理步骤。然而,当前的私有数据对齐方案,主要是基于电路的,存在高通信开销,并且经常需要传输潜在的千兆字节的数据。在本文中,我们提出了一种轻量级的专用数据对齐协议(称为SC-PSI),可以克服通信瓶颈。具体来说,SC-PSI涉及四个阶段的计算,包括数据预处理、数据外包、私有集成员(PSM)评估和电路计算(CC)。与先前的工作一样,SC-PSI的主要开销主要位于后两个阶段。改进之处在于SC-PSI利用函数秘密共享技术开发了PSM协议,避免了计算交集集成员的多轮通信。此外,得益于我们专门设计的PSM协议,SC-PSI在CC阶段不执行复杂的安全比较电路。实验证明,与以前的工作相比,SC-PSI可以节省约61.39%的运行时间和89.61%的通信开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outsourced Privacy-Preserving Data Alignment on Vertically Partitioned Database
In the context of real-world secure outsourced computations, private data alignment has been always the essential preprocessing step. However, current private data alignment schemes, mainly circuit-based, suffer from high communication overhead and often need to transfer potentially gigabytes of data. In this paper, we propose a lightweight private data alignment protocol (called SC-PSI) that can overcome the bottleneck of communication. Specifically, SC-PSI involves four phases of computations, including data preprocessing, data outsourcing, private set member (PSM) evaluation and circuit computation (CC). Like prior works, the major overhead of SC-PSI mainly lies in the latter two phases. The improvement is SC-PSI utilizes the function secret sharing technique to develop the PSM protocol, which avoids the multiple rounds of communication to compute intersection set members. Moreover, benefited from our specially designed PSM protocol, SC-PSI does not to execute complex secure comparison circuits in the CC phase. Experimentally, we validate that compared to prior works, SC-PSI can save around 61.39% running time and 89.61% communication overhead.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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