Revocable DSSE in Healthcare Systems With Range Query Support

IF 5.7 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hanqi Zhang;Yandong Zheng;Chang Xu;Liehuang Zhu;Jiayin Wang
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引用次数: 0

Abstract

With the rapid development of cloud computing, online health monitoring systems are becoming increasingly prevalent. To protect medical data privacy while supporting search operations, Dynamic Searchable Symmetric Encryption (DSSE) technology has been widely used in health monitoring systems. For better monitoring of patient status, keyword range query is also a necessary requirement for the DSSE scheme. Furthermore, in the multi-user setting, user revocation usually leads the owner to download and re-encrypt all indexes, resulting in significant computational overhead. In this paper, we propose a lightweight revocable DSSE scheme with range query support. First, we propose a novel and privacy-preserving range query algorithm that defends plaintext inference attacks. Second, we design a singly linked list structure based on delegatable pseudorandom functions and key-updatable pseudorandom functions, which support lightweight user revocation. Rigorous security analysis proves the security of our proposed range query scheme and demonstrates that our scheme can achieve forward and backward privacy. Experimental evaluations show that our scheme is highly efficient.
具有范围查询支持的医疗保健系统中可撤销的DSSE
随着云计算的快速发展,在线健康监测系统越来越普遍。为了在支持搜索操作的同时保护医疗数据隐私,动态可搜索对称加密(DSSE)技术已广泛应用于健康监测系统中。为了更好地监测患者状态,关键字范围查询也是DSSE方案的必要要求。此外,在多用户设置中,用户撤销通常会导致所有者下载并重新加密所有索引,从而导致大量的计算开销。本文提出了一种支持范围查询的轻量级可撤销DSSE方案。首先,我们提出了一种新的保护隐私的范围查询算法来防御明文推理攻击。其次,基于可委派伪随机函数和可键更新伪随机函数设计了支持轻量级用户撤销的单链表结构。严格的安全性分析证明了我们提出的范围查询方案的安全性,并证明了我们的方案可以实现前向和后向隐私。实验结果表明,该方案是高效的。
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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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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