Fully-incremental public key encryption with adjustable timed-release keyword search

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2025-01-27 DOI:10.1016/j.ins.2025.121887
Tiancheng Zhu , Jiabei Wang , Yuting Xiao , Yiwen Gao , Yongbin Zhou , Jian Weng
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引用次数: 0

Abstract

Public Key Encryption with Keyword Search (PEKS) is a promising technique that enables secure search over encrypted data. However, recent legislation mandates specific activation times for information, necessitating time-controlled retrieval. This creates a pressing need to integrate timed-release control into PEKS, allowing data uploaders to “send indices to the future”, ensuring that secure, searchable indices for specific keywords can only be searched after the designated release-time. Existing approaches or straightforward candidates have several limitations: they either lack cryptographic search control and precise policy enforcement, fail to support flexible and efficient policy adjustment, or exhibit inefficiencies in index/key size and search complexity. In this paper, we formalize a novel variant called Fully-Incremental Public Key Encryption with Timed-Release Keyword Search (Fi-PETRKS), which well captures the functionalities, efficiency, and security requirements. Notably, the processes of functionalities are all incremental, ensuring that the size of token used for each adjustment remains O(1). We propose a concrete Fi-PETRKS construction which is secure against full keyword guessing attacks. Furthermore, we introduce an enhanced version, Fi-PETRKS+, which offers sub-linear search efficiency. Both theoretical analysis and experimental results demonstrate the practicality of our scheme.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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