Journal of Information Security and Applications最新文献_第4页

Anomaly detection for blockchain nodes based on eBPF and fine-tuning large language model 基于eBPF和大语言模型微调的区块链节点异常检测

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1016/j.jisa.2025.104329

Jincheng Su , Zhide Chen , Kexin Zhu , Chen Feng

While blockchain technology is widely used across various fields, it faces growing security challenges. Traditional blockchain anomaly detection methods, such as log analysis and fixed pattern recognition, struggle to handle complex and dynamic attack techniques. This paper proposes the Blockchain Live Anomaly Detection with eBPF and LLMs (BLAD-eLLM) scheme, which combines the efficient data capture capabilities of extended Berkeley Packet Filter (eBPF) technology for kernel-level security monitoring with the deep text understanding and semantic analysis power of large language models (LLMs) to enhance the network layer security of blockchain nodes. The proposed approach analyzes blockchain network activities comprehensively, aiming for accurate identification of potential anomalous behaviors. Furthermore, a knowledge-enhanced reasoning framework is developed, integrating Retrieval-Augmented Generation (RAG) for contextual blockchain threat intelligence and Chain-of-Thought (COT) prompting for multi-step attack analysis while employing Weight-Decomposed Low-Rank Adaptation (DoRA) based prompt fine-tuning to optimize the LLMs’ domain-specific adaptability and detection accuracy. Experimental results demonstrate that the proposed scheme significantly improves blockchain anomaly detection accuracy while maintaining minimal impact on system performance, ensuring the stability and security of the blockchain system.

区块链技术在各个领域得到广泛应用的同时，也面临着越来越多的安全挑战。传统的区块链异常检测方法，如日志分析和固定模式识别，难以处理复杂的动态攻击技术。本文提出了区块链实时异常检测与eBPF和llm （blade - ellm）方案，该方案将扩展伯克利包过滤（eBPF）技术用于内核级安全监控的高效数据捕获能力与大型语言模型（llm）的深度文本理解和语义分析能力相结合，以增强区块链节点的网络层安全性。该方法全面分析区块链网络活动，旨在准确识别潜在的异常行为。此外，开发了一个知识增强推理框架，集成了用于上下文区块链威胁情报的检索增强生成（RAG）和用于多步攻击分析的思维链（COT）提示，同时采用基于权重分解的低秩自适应（DoRA）提示微调来优化llm的特定领域适应性和检测精度。实验结果表明，该方案在保证区块链系统稳定性和安全性的同时，显著提高了区块链异常检测精度。

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引用次数: 0

SAB:A stealing and robust backdoor attack based on steganographic algorithm against federated learning SAB：一种基于隐写算法的针对联邦学习的窃取和鲁棒后门攻击

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.jisa.2025.104353

Weida Xu , Yang Xu , Sicong Zhang

Federated learning, an innovative network architecture designed to safeguard user privacy, is gaining widespread adoption in technology. However, given the existence of backdoor attacks in federated learning, exploring the security of federated learning is significant. Nevertheless, the backdoors investigated in current federated learning research can be readily detected by human inspection or resisted by detection algorithms. Accordingly, a new goal has been set to develop stealthy, robust federated learning backdoor attacks. This paper introduces a novel approach, SAB, explicitly tailored for backdoor attacks in federated learning, presenting an alternative gradient updating mechanism. SAB attack based on steganographic algorithm, using image steganographic algorithm to build a full-size trigger to improve the accuracy of backdoors and use multiple loss joint computation to produce triggers. SAB exhibits smaller distances to benign samples and greater imperceptibility to the human eye. As such, our triggers can mitigate or evade specific backdoor defense methods. In SAB, the bottom-95 % method is applied to extend the lifespan of backdoor attacks. It updates the gradient on minor value points to reduce the probability of being cleaned. Finally, the generalization of backdoors is enhanced with Sparse-update to improve the backdoor accuracy.

联邦学习是一种创新的网络架构，旨在保护用户隐私，在技术上得到广泛采用。然而，鉴于联邦学习中存在后门攻击，探索联邦学习的安全性具有重要意义。然而，在目前的联邦学习研究中调查的后门可以很容易地被人工检查检测到或被检测算法抵制。因此，一个新的目标被设定为开发隐形的、健壮的联邦学习后门攻击。本文介绍了一种新的方法，SAB，明确针对联邦学习中的后门攻击，提出了一种替代的梯度更新机制。基于隐写算法的SAB攻击，利用图像隐写算法构建全尺寸触发器来提高后门的精度，并利用多重损失联合计算产生触发器。SAB与良性样本的距离较小，人眼不易察觉。因此，我们的触发器可以减轻或逃避特定的后门防御方法。在SAB中，采用底部95%方法来延长后门攻击的生命周期。它更新小值点的梯度，以减少被清洗的概率。最后，利用稀疏更新增强后门算法的泛化能力，提高后门算法的准确率。

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引用次数: 0

A lattice-based dual blockchain anonymous authentication scheme with forward security and revocability for VANETs 一种基于格的双向区块链匿名认证方案，具有前向安全性和可撤销性

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.jisa.2025.104369

Xiuhua Lu , Jingzhuo Zhang , Shuanggen Liu , Yanzhe Dong , Xijie Lu , Junzhong Liu

Vehicular Ad-hoc Networks (VANETs) play a vital role in Intelligent Transportation Systems (ITS) by improving traffic safety and efficiency. However, its open communication channels and high-speed mobility introduce privacy and security vulnerabilities, which makes anonymous authentication particularly important for privacy protection and secure communication. Nevertheless, existing anonymous authentication schemes face significant limitations in resisting single point of failure and supporting distributed storage of authentication messages. Moreover, most of these schemes rely on traditional number-theoretic assumptions, and lack resistance to quantum attacks. We propose a lattice-based dual blockchain anonymous authentication scheme with forward security and revocability for VANETs. The dual blockchain framework logically decouples vehicle identities from their mobility patterns, thereby enhancing anonymity. Forward security is obtained by leveraging bonsai trees structure, ensuring that exposure of current secret key of a vehicle does not affect the authenticity of previously transmitted messages. To address the issue of vehicle misbehavior or credential compromise, the scheme also supports revocability, allowing the system to efficiently and anonymously exclude malicious vehicles from the network without affecting honest participants. Our scheme is rigorously proven to achieve correctness, full anonymity and forward secure traceability. Experimental evaluations show its balanced performance in terms of security and efficiency.

车辆自组织网络（VANETs）通过提高交通安全和效率在智能交通系统（ITS）中发挥着至关重要的作用。然而，其开放的通信通道和高速的移动性带来了隐私和安全漏洞，这使得匿名认证对隐私保护和安全通信尤为重要。然而，现有的匿名身份验证方案在抵抗单点故障和支持身份验证消息的分布式存储方面面临着很大的限制。此外，这些方案大多依赖于传统的数论假设，缺乏对量子攻击的抵抗力。提出了一种基于格的双区块链匿名认证方案，具有前向安全性和可撤销性。双区块链框架从逻辑上将车辆身份与其移动模式解耦，从而增强了匿名性。前向安全性是利用盆景树结构实现的，确保车辆当前密钥的暴露不会影响先前传输消息的真实性。为了解决车辆不当行为或凭证泄露的问题，该方案还支持可撤销性，允许系统在不影响诚实参与者的情况下有效和匿名地从网络中排除恶意车辆。我们的方案被严格证明可以实现正确性、完全匿名性和前向安全可追溯性。实验结果表明，该算法在安全性和效率方面达到了平衡。

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引用次数: 0

Seeing the invisible: Detection of stealth DoS attacks using variational U-Net-like models 看到不可见的：使用变分u - net样模型检测隐形DoS攻击

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jisa.2025.104356

Enrico Cambiaso , Francesco Folino , Massimo Guarascio , Angelica Liguori , Antonino Rullo

The increasing sophistication of cyberattacks targeting companies and organizations continues to challenge the effectiveness of modern defense systems. Among these threats, slow Denial-of-Service (slow DoS) attacks are particularly difficult to detect, as they rely on evasion strategies that add significant complexity to cybersecurity efforts. Modern intrusion detection systems, especially those based on deep learning, have become essential tools in combating such attacks. However, their performance is often hindered by challenges such as limited data availability, noisy inputs, and the presence of out-of-distribution samples. Furthermore, their dependence on large labeled datasets makes detecting subtle or rare attack patterns particularly challenging. To overcome these limitations, this work proposes a novel unsupervised deep learning framework for detecting slow DoS attacks. The proposed approach incorporates a customized preprocessing pipeline to improve input data quality and leverages a sparse variational U-Net-like architecture for robust anomaly identification. Extensive experiments conducted on three real-world datasets demonstrate the ability of the framework to accurately and efficiently detect slow DoS attacks, highlighting its robustness, generalizability, and practical suitability for deployment in operational environments.

针对公司和组织的网络攻击越来越复杂，继续挑战现代防御系统的有效性。在这些威胁中，慢速拒绝服务（slow DoS）攻击尤其难以检测，因为它们依赖于规避策略，这大大增加了网络安全工作的复杂性。现代入侵检测系统，特别是基于深度学习的入侵检测系统，已经成为打击此类攻击的重要工具。然而，它们的性能经常受到诸如有限的数据可用性、噪声输入和分布外样本的存在等挑战的阻碍。此外，它们对大型标记数据集的依赖使得检测微妙或罕见的攻击模式特别具有挑战性。为了克服这些限制，本工作提出了一种新的无监督深度学习框架，用于检测慢速DoS攻击。该方法采用自定义预处理管道来提高输入数据质量，并利用稀疏变分u - net类架构进行鲁棒异常识别。在三个真实数据集上进行的大量实验证明了该框架能够准确有效地检测慢速DoS攻击，突出了其鲁棒性、通用性和在操作环境中部署的实际适用性。

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引用次数: 0

A high efficiency AVX2-optimized engineering of the post-quantum digital signature CROSS 后量子数字签名CROSS的高效avx2优化工程

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.jisa.2025.104331

Alessandro Barenghi, Marco Gianvecchio, Gerardo Pelosi

Post-quantum cryptosystems are currently attracting a significant amount of research efforts due to the continuous improvements in quantum computing technologies, and the inherent high inertia characterizing the replacement of cryptographic standards. This situation has pushed large standardization bodies, such as the USA National Institute of Standards and Technology (NIST), to open standardization competitions to foster proposals and public scrutiny of new quantum-resistant cryptosystems and digital signatures. Whilst NIST has chosen, after four selection rounds (November 2017 - June 2023), three digital signature algorithms, in July 2023 it started a new selection process as the chosen candidates either rely exclusively on lattice-based computationally hard problems, or have unsatisfactory performance figures. In this work, we tackle the performance engineering of the Codes and Restricted Objects Signature Scheme (CROSS), which has been admitted to the second round of selection by NIST in October 2024. We propose a set of techniques to optimize software realizations of CROSS, targeting the AVX2 ISA extension by Intel, as requested by NIST; exploiting fully our choices on the signature scheme parameters, as part of the design team. We note that these techniques are general enough to be ported to other vector ISA extensions (e.g., ARM Neon). We provide a complete performance validation of our realization both with dedicated microbenchmarks as well as full end-to-end TLS benchmarks with realistic network delays. Our results show that CROSS is competitive with each of the already standardized post-quantum signature schemes as well as with the other schemes still under evaluation in the second selection round.

由于量子计算技术的不断改进，以及加密标准替换所固有的高惯性，后量子密码系统目前吸引了大量的研究工作。这种情况促使大型标准化机构，如美国国家标准与技术研究所（NIST），开放标准化竞赛，以促进新的抗量子密码系统和数字签名的提案和公众监督。经过四轮（2017年11月至2023年6月）的选拔，NIST选择了三种数字签名算法，但在2023年7月，它开始了一个新的选择过程，因为被选中的候选算法要么完全依赖于基于格子的计算难题，要么性能不理想。在这项工作中，我们解决了代码和受限对象签名方案（CROSS）的性能工程，该方案已于2024年10月被NIST录取进入第二轮选择。根据NIST的要求，我们提出了一套技术来优化CROSS的软件实现，针对英特尔的AVX2 ISA扩展；作为设计团队的一部分，充分利用我们对签名方案参数的选择。我们注意到，这些技术足够通用，可以移植到其他矢量ISA扩展（例如，ARM Neon）。我们通过专用的微基准测试和具有实际网络延迟的完整端到端TLS基准测试对我们的实现进行了完整的性能验证。我们的研究结果表明，CROSS与每个已经标准化的后量子签名方案以及仍在第二轮评选中评估的其他方案具有竞争力。

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引用次数: 0

A taxonomy of graph-based risk, vulnerability, and attack assessment methods in IoT systems 物联网系统中基于图的风险、漏洞和攻击评估方法的分类

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-09 DOI: 10.1016/j.jisa.2025.104360

Ferhat Arat , Aykut Karakaya , Sedat Akleylek

This paper presents a comprehensive Systematic Literature Review (SLR) focusing on graph-based security and risk assessment approaches for IoT systems, utilizing a comprehensive taxonomy. In addition to elucidating IoT fundamentals and principles, we offer an expansive framework with a specific emphasis on graph-IoT-security concepts, contributing to the systematic organization of information. Unlike existing reviews, our focus is explicitly directed towards using graph theory for security assessment. Moreover, no systematic literature review has been conducted to specifically analyze security assessment approaches of IoT systems using graph methods, excluding other theoretical and applied techniques.

The objective is to describe, synthesize, and compare security developments in IoT from the perspective of various research questions. To the best of our knowledge, this paper represents the first SLR that concentrates on graph-based risk and vulnerability assessments. The primary objective of the SLR is to provide a comprehensive overview for researchers to identify attack graph or attack tree-based approaches. We delineate the IoT paradigm from a security perspective, highlighting various types of attacks detected by graph-based techniques. Furthermore, we present a comparative analysis of existing graph-based security evaluation mechanisms designed for IoT under various metrics, such as procedural performance, runtime, complexity, and other algorithmic overheads. Finally, we reveal issues and challenges regarding open problems that have not been considered and addressed in existing studies.

本文介绍了一篇全面的系统文献综述（SLR），重点介绍了基于图的物联网系统安全和风险评估方法，采用了全面的分类法。除了阐明物联网的基本原理和原则外，我们还提供了一个广泛的框架，特别强调图形物联网安全概念，有助于系统地组织信息。与现有的评论不同，我们的重点明确地指向使用图论进行安全评估。此外，除了其他理论和应用技术外，没有系统的文献综述专门使用图方法分析物联网系统的安全评估方法。目的是从各种研究问题的角度描述、综合和比较物联网的安全发展。据我们所知，这篇论文代表了第一个专注于基于图的风险和脆弱性评估的单反。SLR的主要目标是为研究人员提供一个全面的概述，以确定攻击图或基于攻击树的方法。我们从安全的角度描述了物联网范式，重点介绍了基于图形的技术检测到的各种类型的攻击。此外，我们在各种指标（如程序性能、运行时间、复杂性和其他算法开销）下，对为物联网设计的现有基于图的安全评估机制进行了比较分析。最后，我们揭示了在现有研究中尚未考虑和解决的开放性问题的问题和挑战。

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引用次数: 0

Verifiable cross-modal searchable encryption via hierarchical spherical tree with beam search 带波束搜索的分层球树可验证跨模态可搜索加密

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.jisa.2026.104373

Yuzhe Wang, Jiachen Shen, Xiaolei Dong, Zhenfu Cao

Large-scale multimedia data are widely outsourced to cloud services to support cross-modal retrieval, such as text-to-image search. However, the cloud server is not fully trusted, which raises privacy concerns. Cross-modal searchable encryption (CMSE) enables retrieval over encrypted data without revealing content or query information. In large-scale settings, efficient similarity search over encrypted high-dimensional embeddings remains challenging. Existing indexing approaches, such as locality-sensitive hashing and tree-based methods, face trade-offs between retrieval quality and efficiency. Moreover, a malicious server may skip computations or return incomplete results, making verifiability an important requirement. This paper proposes VCSE-HST, a verifiable cross-modal searchable encryption scheme. VCSE-HST builds a hierarchical spherical tree index for fast pruning and uses beam search to explore multiple candidate paths, achieving both high efficiency and high retrieval quality on large datasets. The scheme provides a dual verification mechanism: score correctness verification for encrypted similarity scores and execution integrity verification based on Merkle commitment. Security analysis demonstrates that VCSE-HST achieves index confidentiality and trapdoor indistinguishability. Experimental evaluation validates that VCSE-HST attains substantial efficiency gains over linear search while preserving retrieval accuracy.

大规模多媒体数据被广泛外包给云服务，以支持跨模式检索，如文本到图像的搜索。然而，云服务器不是完全可信的，这引起了隐私问题。跨模式可搜索加密（CMSE）支持在不泄露内容或查询信息的情况下对加密数据进行检索。在大规模环境下，对加密的高维嵌入进行有效的相似性搜索仍然是一个挑战。现有的索引方法，如位置敏感散列和基于树的方法，面临着检索质量和效率之间的权衡。此外，恶意服务器可能跳过计算或返回不完整的结果，这使得可验证性成为一个重要的需求。提出了一种可验证的跨模态可搜索加密方案VCSE-HST。VCSE-HST构建了层次球形树索引进行快速剪枝，并利用束搜索探索多个候选路径，在大数据集上实现了高效率和高检索质量。该方案提供了双重验证机制：加密相似度分数的分数正确性验证和基于默克尔承诺的执行完整性验证。安全性分析表明，VCSE-HST实现了索引机密性和陷门不可分辨性。实验评估验证了VCSE-HST在保持检索精度的同时获得了比线性搜索更大的效率提升。

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引用次数: 0

Robust DSSA-Net framework for splicing detection in image encryption domain 鲁棒dsa - net框架在图像加密领域的拼接检测

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 10.1016/j.jisa.2025.104341

Debolina Ghosh , Ruchira Naskar , Bidesh Chakraborty

One of the most common and widespread types of digital image fraudulence is image splicing attack, which combines contents from several sources into a single composite to deceive viewers. In this work, we address the problem of image splicing detection by proposing a robust deep learning-based framework. Specifically, the detector is built to operate for splicing detection on encrypted images, considering the growing usage of images in encrypted domains in IoT environments. The proposed model operates at two levels of granularity. At level 1, the model answers a binary question: whether an encrypted image is spliced or authentic. We achieve this by adding Self-Attention and Squeeze-and-Excitation modules to a custom DenseNet backbone, which enhances feature discrimination and localization in difficult scenarios like compression and encryption. At level 2, we determine the encryption technique that the image under question has undergone. Dense connections and attention mechanisms allow the network to focus on subtle splicing artifacts, on encrypted image inputs. Our results show that learnable patterns are persistent in encrypted image representations, resulting in robust classification performance.

最常见和最广泛的数字图像欺诈类型之一是图像拼接攻击，它将来自多个来源的内容组合成单个合成物来欺骗观众。在这项工作中，我们通过提出一个基于深度学习的鲁棒框架来解决图像拼接检测问题。具体来说，考虑到物联网环境中加密领域中图像的使用越来越多，该检测器旨在对加密图像进行拼接检测。所建议的模型在两个粒度级别上运行。在第一级，模型回答一个二元问题：加密图像是拼接的还是真实的。我们通过在自定义DenseNet骨干网中添加自关注和压缩激励模块来实现这一点，这增强了在压缩和加密等困难场景下的特征识别和定位。在第2级，我们确定所讨论的图像经过的加密技术。密集的连接和注意机制允许网络专注于细微的拼接工件，加密的图像输入。我们的研究结果表明，可学习模式在加密图像表示中是持久的，从而产生了鲁棒的分类性能。

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引用次数: 0

Reversible data hiding in PDF files by overlapping characters 通过重叠字符隐藏在PDF文件中的可逆数据

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.jisa.2026.104375

Wendi Zhu, Mohamed Mazeen Mujthaba, KokSheik Wong

Most reported PDF-based data hiding methods make subtle changes to existing PDF elements to conceal data, where the changes are predominantly made to the existing ‘TJ’ operator in a typical PDF document. However, this choice limits the volume of data that can be hidden, and most methods inevitably introduce visual distortion to the PDF document. Therefore, in this work, we propose a novel method called OPAC to hide data in PDF by means of overlapping characters, which is the first of its kind for data hiding purposes. Specifically, overlapping employs a series of technical steps to strategically stack characters. First, all 26 characters in the English alphabet are arranged into a 4 × 7 table based on their frequency of occurrences, where each character can be referred to by specifying the column and row in which it appears. Subsequently, the encrypted message is hidden, one character at a time, by superimposing a special symbol called anchor onto a specific character to indicate the column number, and specific combination of the ‘Tf’ and ‘Tz’ operators is added to the PDF stream to indicate the row number. Meanwhile, the newly added ‘Tf’ and ‘Tz’ operators are further manipulated to reduce the size and width of anchor to allow for complete character overlapping. We evaluate the performance of the proposed method OPAC using 220 PDF documents generated from various famous texts. On average, OPAC can hide 3877 characters at the expense of 0.224 MB increase in PDF file size. Furthermore, experiment results confirmed that OPAC achieves distortion-free data hiding and reversibility. Moreover, OPAC is empirically verified to be resistant to several common PDF processing and alterations.

大多数报告的基于PDF的数据隐藏方法对现有的PDF元素进行细微的更改以隐藏数据，其中主要是对典型PDF文档中现有的‘ TJ ’操作符进行更改。但是，这种选择限制了可以隐藏的数据量，并且大多数方法不可避免地会给PDF文档带来视觉失真。因此，在这项工作中，我们提出了一种新的方法，称为OPAC，通过重叠字符来隐藏PDF中的数据，这是第一个用于数据隐藏的方法。具体来说，重叠使用了一系列技术步骤来策略性地堆叠字符。首先，根据出现频率将英文字母表中的所有26个字符排列到4 × 7表中，每个字符可以通过指定其出现的列和行来引用。随后，通过将称为anchor的特殊符号叠加到指示列号的特定字符上来隐藏加密消息，每次隐藏一个字符，并将‘ Tf ’和‘ Tz ’操作符的特定组合添加到PDF流中以指示行号。同时，对新增加的“Tf”和“Tz”操作符进行了进一步的操作，以减小锚的大小和宽度，从而允许完整的字符重叠。我们使用220个由各种著名文本生成的PDF文档来评估所提出的方法OPAC的性能。平均而言，OPAC可以隐藏3877个字符，代价是PDF文件大小增加0.224 MB。实验结果表明，OPAC实现了无失真的数据隐藏和可逆性。此外，经验证明OPAC可以抵抗几种常见的PDF处理和更改。

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引用次数: 0

A Novel Privacy-Preserving user information queries scheme with functional policy 一种新的带功能策略的保隐私用户信息查询方案

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1016/j.jisa.2025.104359

Yuhang Lei , Rui Shi , Yang Yang , Chunjie Cao , Huamin Feng

Privacy-preserving information queries enable a requester to obtain only the value f(x) computed over sensitive data x, while preventing disclosure of the underlying records. Existing approaches typically reveal full data, incur high on-chain overhead, or lack fair and verifiable delivery of function outputs. We propose a general-purpose, blockchain-compatible framework that ensures the requester learns only f(x) with no extra leakage and that the provider receives fair payment. The design integrates Adaptor Signatures (AS) for fair exchange and Inner-Product Functional Encryption (IPFE) for fine-grained function extraction. The framework is domain-agnostic and applicable to privacy-sensitive applications such as medical insurance and financial risk assessment. We formally prove advertisement soundness, unforgeability, witness extractability, and witness privacy. A prototype demonstrates linear scalability up to ℓ=100,000. We conduct a comparative evaluation between our scheme and related works under typical attribute dimensions

ℓ = 10, 20, 30

. The results show that, under the same dimensional settings, our scheme achieves a 2.5×-6× performance improvement. Meanwhile, the storage overhead of our solution ranges from 1.6 KB to 4.1 KB, which is slightly lower than that of existing schemes (2.7-4.0 KB), and the on-chain cost consistently remains a fixed 64 bytes. These findings demonstrate that our approach provides significant practical advantages in low-dimensional, high-frequency medical query scenarios.

保护隐私的信息查询使请求者只能获得对敏感数据x计算的值f(x)，同时防止底层记录的泄露。现有的方法通常会显示完整的数据，导致高链上开销，或者缺乏公平和可验证的功能输出交付。我们提出了一个通用的、区块链兼容的框架，确保请求者只学习f(x)，没有额外的泄漏，并且提供者收到公平的支付。该设计集成了用于公平交换的适配器签名（AS）和用于细粒度功能提取的内部产品功能加密（IPFE）。该框架与领域无关，适用于隐私敏感的应用程序，如医疗保险和金融风险评估。我们正式证明广告的稳健性、不可伪造性、证人可提取性和证人隐私性。一个原型演示了线性可扩展性，最高可达100,000。在典型属性维数=10、20、30的情况下，我们将我们的方案与相关工作进行了比较评价。结果表明，在相同的维度设置下，我们的方案实现了2.5×-6×性能提升。同时，我们的解决方案的存储开销范围从1.6 KB到4.1 KB，略低于现有方案（2.7-4.0 KB），并且链上成本始终保持固定的64字节。这些发现表明，我们的方法在低维、高频医疗查询场景中具有显著的实用优势。

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引用次数: 0