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Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward security

IEEE INTERNET OF THINGS JOURNAL 2025年第3期12卷 2633-2645页

作者： Zhao, Chenbin Du, Ruiying He, Kun Chen, Jing Li, Jiguo Liu, Ximeng Ning, Jianting Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan 430072 Peoples R China Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Fujian Normal Univ Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Fujian Peoples R China Fujian Normal Univ Coll Math & Comp Sci Fujian Prov Key Lab Network Secur & Cryptol Fuzhou Peoples R China

In the realm of secure data outsourcing, verifiable dynamic searchable symmetric encryption (VDSSE) enables a client to verify search results obtained from an untrusted server while protecting the data privacy. Nevertheless, the storage cost of verification structure in some schemes escalates linearly with the number of keywords, and the generation of proofs demands a substantial number of exponentiation operations. Moreover, some schemes overlook forward and backward security in the dynamic database. In this article, we introduce FB-VDSSE, an advanced VDSSE scheme that ensures both forward and backward security. Specifically, we introduce an efficient accumulation commitment verification structure (AC-VS) that attains a commitment verification value with a constant-size storage cost. Based on the AC-VS, we further propose a forward and backward secure VDSSE scheme. Within this scheme, the server exclusively generates a membership proof at the corresponding index of the vector, reducing the computation cost associated with the search operation. Finally, we provide the security proof and functional comparison, demonstrating that our scheme effectively ensures forward security, backward security, and verifiability. Additionally, the experimental evaluations underscore the efficiency of our scheme, showcasing its superior performance compared to relevant schemes in practical scenarios.

关键词： Backward security forward security searchable symmetric encryption verifiability Backward security forward security searchable symmetric encryption verifiability

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Private Reachability Queries on Structured Encrypted Temporal Bipartite Graphs

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IEEE TRANSACTIONS ON DEPENDABLE AND securE COMPUTING 2025年第2期22卷 1810-1826页

作者： Wu, Yulin Chen, Lanxiang Chen, Gaolin Mu, Yi Deng, Robert H. Fujian Normal Univ Coll Comp & Cyber Secur Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Peoples R China City Univ Macau Fac Data Sci Taipa 314100 Macao Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore

A temporal bipartite graph is a graph model that incorporates time-related information into its edges, making it suitable for modeling real-world phenomena like disease outbreaks. However, this temporal information is often sensitive. To protect the privacy of graph data, researchers have explored various approaches to preserve privacy in graph queries, with reachability queries being popular and fundamental as they determine the possibility of reaching one node from others in a graph. While privacy-preserving reachability queries have been extensively studied, existing efforts often overlook the valuable attribute information present in both edges and nodes of the graphs. Moreover, reachability queries on temporal bipartite graphs have not received sufficient attention in the literature. To bridge this gap, we propose a novel approach to achieve various private reachability queries on structured encrypted temporal bipartite graphs (RQ-STBG) through a real-world scenario. Specifically, we construct a minimal index using hierarchical 2-hop labels and integrate structured encryption, order-revealing encryption, and garbled Bloom filters to support reachability queries with different label constraints. The proposed scheme is flexible and can cater to the query requirements of diverse users. security analysis and experimental evaluations demonstrate that the proposed scheme achieves both preferable security and efficiency.

关键词： Bipartite graph Encryption Diseases Data privacy security Protection Privacy Computational modeling labeling Indexing Temporal bipartite graph reachability query privacy preserving 2-hop label structured encryption garbled Bloom filte

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Bi-directional temporal graph attention networks for rumor detection in online social networks

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COMPUTING 2025年第1期107卷 1-17页

作者： Zhou, Qiao Lin, Xingpeng Xu, Li Sun, Yan Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Fujian Peoples R China Fujian Normal Univ Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Fujian Peoples R China China Ind Control Syst Cyber Emergency Response Te Beijing 100043 Peoples R China

Online Social networks (OSNs) have become the main platform for individuals to share information, but the rapid dissemination of information has also resulted in an increase in the spread of rumors. Some structure-based deep learning methods focus on the depth of propagation while disregarding the wide disperse patterns of rumors. They are also based on static graphs, which are incompatible for capturing the dynamic nature of real-world rumor propagation. To overcome these challenges, we propose a novel Bi-directional Temporal Graph Attention networks, termed as BTAN. Firstly, we incorporate propagation patterns, dispersion structure, and temporal information into a unified framework for rumor detection. Secondly, we model the temporal evolution patterns of rumors as a graph that evolves within the context of continuous-time dynamic graphs, and employ two Graph Attention networks to capture the information regarding both top-down and bottom-up rumor propagation. Finally, extensive experiments conducted on real-world datasets show the superior performance of our model for rumor detection.

关键词： Rumor detection Attention mechanism Graph neural networks Online social networks

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Local Fault Diagnosis Analysis Based on Block Pattern of Regular Diagnosable networks

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IEEE TRANSACTIONS ON networkING 2025年第2期33卷 849-864页

作者： Lin, Limei Guan, Kaineng Huang, Yanze Hsieh, Sun-Yuan Chen, Gaolin Fujian Normal Univ Coll Comp & Cyber Secur Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

Fault diagnosability can reflect the actual self diagnosing capability of a multiprocessor system better. However, people usually focus on the overall information and neglect the important local information. In order to reflect the locality of a system at a node better, this paper proposes a novel fault diagnosis strategy, called x-block local fault diagnosability (x-BLFD), where the x-block condition requires more than x connected fault-free nodes. Then, we characterize some important properties about the x-BLFD of multiprocessors interconnected networks under the Preparata/Metze/Chien model (P/M/C), and further propose the x-BLFD in an f(x)-extended block network with the minimum (x + 1)-subnetwork degree at some node. We also establish an approximate algorithm to calculate the x-BLFD of a large-scale diagnosable network at some node, and analyze the experimental performance of large-scale networks. Furthermore, we apply our proposed conclusion to obtain the x-BLFD of 16 well-known networks at some node directly under P/M/C, including dual cubes, hierarchical cubic networks, DQcubes, twisted hypercubes, Bicube networks, crossed cubes, folded hypercubes, k-ary n-cubes, balanced hypercubes, BC graphs, (n, k)-star graphs, Cayley graphs generated by transposition trees, bubble-sort star graphs, split-star networks, data center networks, and (n, k)-arrangement graphs. Finally, we compare the x -BLFD with the diagnosability, conditional diagnosability, pessimistic diagnosability, and t/k-diagnosability by a large number of detailed numerical analysis. It can be seen that the x-BLFD is greater than all the other types of fault diagnosabilities.

关键词： Reliability fault diagnosis block local fault diagnosability block local fault diagnosability regular diagnosable networks regular diagnosable networks interconnection networks interconnection networks interconnection networks

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Achieving Efficient Privacy-Preserving Mixed Data Quality Assessment in Mobile Crowdsensing

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3785-3799页

作者： Huang, Chunpu Zhang, Yuanyuan Xiong, Jinbo Bi, Renwan Tian, Youliang Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Fujian Normal Univ Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Guizhou Univ Coll Big Data & Informat Engn State Key Lab Publ Big Data Guiyang 550025 Peoples R China

In mobile crowdsensing (MCS) applications, the single type data is inadequate to reflect the complexities of the real world and meet precise task requirements. Currently, there are few works that focus on mixed data in the context of MCS, and there is no work considering the credit issues of sensing platform. The privacy, fairness, and reliability of assessing the quality of mixed data remain unguaranteed. Therefore, we design a high-efficiency and privacy-preserving mixed data quality assessment scheme which adopts a dual-server architecture, designs secure k-prototype clustering for quality assessment, and conducts anomaly detection to eliminate anomalous data. Furthermore, we design a fair and reliable allocation mechanism to fairly allocate reward to users based on fixed and floating reward mechanisms for incentivizing rational users to submit high-quality mixed data. To prevent payment defaults by the sensing platform, we design verifiable credential to restrict them, ensuring payment fairness and transactional reliability. Finally, through theoretical analysis and experimental evaluation, we demonstrate the effectiveness and security of the proposed scheme. The results indicate that in terms of efficiency, the time overhead of mixed data quality assessment has been significantly reduced by three orders of magnitude compared to existing schemes.

关键词： Sensors Reliability Data integrity Quality assessment Data privacy Reliability engineering Privacy Resource management Crowdsensing Mobile computing Mixed data mobile crowdsensing (MCS) privacy preserving quality assessment

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Video capturing device identification through block-based PRNU matching

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FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION 2025年 52卷

作者： Li, Jian Wang, Fei Ma, Bin Wang, Chunpeng Wu, Xiaoming Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist Educ Natl Supercomp Ctr JinanQilu Univ TechnolShandon Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Ind Network & Informat Syst Jinan Peoples R China

This paper addresses the performance of a PRNU-based (photo response non-uniformity) scheme to identify the capturing device of a video. A common concern is PRNU in each frame being misaligned due to the video stabilization process compensating for unintended camera movements. We first derive the expectation of a similarity measure between two PRNUs: a reference and a test. The statistical analysis of the similarity measure helps us to understand the effect of homogeneous or heterogeneous misalignment of PRNU on the performance of identification for video capturing devices. We notice that dividing a test PRNU into several blocks and then matching each block with a part of the reference PRNU can decrease the negative effect of video stabilization. Hence a block-based matching algorithm for identifying video capturing devices is designed to improve the identification efficiency, especially when only a limited number of test video frames is available. Extensive experimental results prove that the proposed block-based matching algorithm can outperform the prior arts under the same test conditions.

关键词： Forensics Smartphone Video Stabilization PRNU

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Efficient and secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 6001-6013页

作者： Chen, Lin Yang, Yiwei Yang, Li Xu, Chao Miao, Yinbin Liu, Zhiquan Liu, Ximeng Choo, Kim-Kwang Raymond Liang, Zhihong Hong, Chao Guangdong Prov Key Lab Power Syst Network Secur Guangzhou 510080 Peoples R China China Southern Power Grid Co Ltd Elect Power Res Inst Guangzhou 510080 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350117 Peoples R China Fuzhou Univ Fujian Prov Key Lab Informat Secur Network Syst Fuzhou 350117 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA

With the rapid growth of encrypted image data outsourced to cloud servers, achieving data confidentiality and searchability in cloud-assisted Internet of Things (IoT) environments has become increasingly feasible. However, achieving high efficiency and strong security simultaneously over large-scale encrypted image datasets remains a challenge. To address this, we propose a novel efficient and secure content-based image retrieval scheme in cloud-assisted IoT. Specifically, our scheme leverages lattice-based fully homomorphic encryption and homomorphic comparison techniques, utilizing Cheon-Kim-Kim-Song's batch processing and single-instruction-multiple-data capabilities. This approach significantly reduces the overhead of fully homomorphic computations, making the query process computational complexity independent of dataset size under certain conditions. Moreover, by integrating private information retrieval technology, the scheme enhances privacy by hiding access patterns of image data. Formal security analysis demonstrates that our scheme achieves indistinguishability against chosen-plaintext attack (IND-CPA), and extensive experiments based on real datasets confirm that our scheme is both practical and efficient for real-world applications.

关键词： Internet of Things Image retrieval Nearest neighbor methods Indexes Homomorphic encryption Cloud computing security Feature extraction Privacy Data privacy Content-based image retrieval (CBIR) encrypted image data fully homomorphic encryption (HE) homomorphic comparison private information retrieval (PIR)

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Fair-and-Exculpable-Attribute-Based Searchable Encryption With Revocation and Verifiable Outsourced Decryption Using Smart Contract

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4302-4317页

作者： Chen, Liqing Xu, Shiguo Zhang, Hao Weng, Jian Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Jiangsu Peoples R China Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Fujian Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China

Users are inclined to outsource their data to the cloud due to the rapid growth of data in response to the growing use of cloud computing and the Internet of Things (IoT). One of the primary issues for users is the secure sharing of personal data. Access control and fine-grained sharing of encrypted data can be accomplished with attribute-based encryption (ABE). However, the computational cost required for ABE decryption hinders its widespread deployment on lightweight devices. For lightweight mobile devices, outsourcing computation is a highly attractive technical that outsources heavy decryption tasks to the cloud. The majority of current outsourced ABE schemes, however, only verify whether the cloud returns the correct decryption result, and lack a mechanism to prevent mobile devices from maliciously accusing the cloud of returning incorrect results, and overlook fairness between mobile devices and the cloud. Furthermore, the increasing number of encrypted files stored in the cloud will impede efficient query processing. Moreover, attribute revocation and privacy protection are also crucial. Therefore, this paper proposes fair-and-exculpable-attribute-based searchable encryption with revocation and verifiable outsourced decryption (FE-ABSE-RV) Using Smart Contract, and constructs a concrete FE-ABSE-RV scheme. The scheme is capable of withstanding chosen plaintext attack and chosen keyword attack according to security analysis. Theoretical analysis and simulation experiments illustrate that the FE-ABSE-RV scheme is more expressive, efficient, and practical.

关键词： Cloud computing Encryption Mobile handsets Smart contracts Internet of Things Servers Cryptography Outsourcing Costs Data privacy Attribute revocation attribute-based searchable encryption privacy protection smart contract verifiable outsourced decryption

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Dynamic malware detection based on supervised contrastive learning

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Yang, Shumian Yang, Yongqi Zhao, Dawei Xu, Lijuan Li, Xin Yu, Fuqiang Hu, Jiarui Qilu Univ Technol Key Lab Comp Power Network & Informat Secur Shandong Acad Sci Minist EducShandong Comp Sci CtrNatl Supercomp C Jinan 250014 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Ind Network & Informat Syst Jinan 250014 Peoples R China

Application Programming Interface (API) calls record interactions between a program and the operating system or other programs during runtime. Due to this precise tracking capability, API call information is extensively utilized in malware detection. However, most approaches only focus on the API names or simply combine them with API parameters, leading to insufficient semantic information extraction. Additionally, with the continuous development of network technology, the behavioral feature differences between malware and benign software are gradually blurring, which makes it difficult to detect hard samples (e.g., well-disguised or atypical malware) in static analysis or simple behavioral patterns. Therefore, in this paper, we propose DMASCL, a framework that utilizes Dynamic Malware Analysis based on API calls and Supervised Contrastive Learning techniques, which encodes semantic as well as statistical features in each sample, dynamically compares samples from different categories, learns inter- sample differences and performs classification. In particular, we combine API names with API parameters to construct API sentences containing rich semantic information, and propose a hybrid feature encoder for obtaining the semantics and statistical features of API parameters. We then apply supervised contrastive learning techniques for further feature learning, utilizing Gaussian noise to construct contrast tasks. The model is optimized by combining cross- entropy loss for classification with supervised contrastive loss to reinforce relationships between samples, thus enhancing the model's ability to recognize malicious behavior. Our method achieves a 98.42% F1-score and a 98.03% recall in Dataset 1. It achieves a 99.59% F1-score and a 98.86% recall in Dataset 2. The experimental results show an increase in accuracy of 1.05% and 2.27%, respectively, compared to the state-of-the-art method.

关键词： Malware detection Dynamic analysis Supervised contrastive learning API call Deep learning

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PH-MG-ABE: A Flexible Policy-Hidden Multigroup Attribute-Based Encryption Scheme for secure Cloud Storage

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 2146-2157页

作者： Li, Jiguo Zhang, Enfan Han, Jinguang Zhang, Yichen Shen, Jian Fujian Normal Univ Coll Comp & Cyber Secur Sch Math & Stat Key Lab Analyt Math & ApplicatMinist Educ Fuzhou 350117 Peoples R China Fujian Normal Univ Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Peoples R China Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China

Ciphertext-policy attribute-based encryption (CP-ABE) has attracted significant attention due to its fine-grained access control capabilities, which are highly compatible with cloud computing. Most enterprises utilizing cloud storage technology consist of multiple user groups. However, the current multigroup CP-ABE scheme may pose a risk of sensitive information leakage due to the plaintext access policy mechanisms. To mitigate this issue, it is necessary to conceal access policies. In this article, we propose a flexible policy-hidden multigroup attribute-based encryption (PH-MG-ABE) scheme that enables unique multigroup operations, such as group merging and splitting without affecting user keys. Each attribute in the access policy is divided into attribute values and attribute names. The proposed scheme achieves partial policy hiding by concealing the attribute values. Our scheme allows to directly revoke and join arbitrary numbers of users. In order to reduce the local decryption burden for users, the heavy decryption tasks are outsourced to cloud servers and correctness of the outsourced decryption is verifiable. We prove that our scheme is indistinguishable against under chosen plaintext attacks secure (IND-CPA) based on the decisional q-bilinear Diffie-Hellman exponent assumption. In addition, the proposed scheme appears to be efficient through the performance evaluation.

关键词： Cloud computing Encryption security Servers Internet of Things Outsourcing Electronic mail Ciphertext-policy attribute-based encryption (CP-ABE) cloud storage multigroup operations policy-hidden revocation

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