Journal of Information Security and Applications最新文献

{"title":"VeriKNN: A verifiable and efficient secure k-NN query scheme via homomorphic encryption in dual-cloud environments","authors":"Wankang Bao,&nbsp;Zhongxiang Zheng","doi":"10.1016/j.jisa.2025.104265","DOIUrl":"10.1016/j.jisa.2025.104265","url":null,"abstract":"<div><div>With the widespread adoption of cloud computing, data privacy protection has become a critical issue, especially for encrypted retrieval tasks in cloud environments. This paper proposes <strong>VeriKNN</strong>, a secure, efficient, and verifiable <span><math><mi>k</mi></math></span>-nearest neighbor (kNN) query scheme based on homomorphic encryption. The scheme introduces a verification mechanism that specifically ensures communication integrity between servers under an honest-but-curious threat model. VeriKNN first constructs a Voronoi diagram and partitions it into encrypted grids for coarse filtering. Then, it builds a multi-layer index of neighboring cells to enable fine-grained search. To address the challenges of computational efficiency and security, we propose a novel set of secure protocols specifically designed for dual-cloud collaboration, significantly improving retrieval performance. Extensive experiments demonstrate that VeriKNN outperforms the state-of-the-art SVK scheme by up to 3000 times in speed across various dataset sizes and <span><math><mi>k</mi></math></span>-values, and is applicable not only to two-dimensional data but also to high-dimensional data. Compared to the HFkNN scheme, VeriKNN achieves a slight reduction in accuracy but offers a 500-fold increase in speed, with the performance gap widening as dataset size increases, highlighting its superior scalability and efficiency.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104265"},"PeriodicalIF":3.7,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum-resilient blockchain framework for privacy-preserving genomic data sharing and analysis","authors":"Puja Das ,&nbsp;Chitra Jain ,&nbsp;Abdullah M. AlShahrani ,&nbsp;Moutushi Singh ,&nbsp;Md. Zeyaullah ,&nbsp;Hytham Hummad","doi":"10.1016/j.jisa.2025.104245","DOIUrl":"10.1016/j.jisa.2025.104245","url":null,"abstract":"<div><div>The rapid expansion of Next-Generation Sequencing (NGS) technologies has positioned genomic data at the forefront of personalized healthcare. Yet, its sensitive nature introduces significant challenges related to security, privacy, and integrity—especially in the face of emerging quantum computing threats. This paper presents a quantum-resilient blockchain framework that integrates post-quantum cryptographic primitives, privacy-preserving machine learning, and decentralized data governance to ensure secure sharing and inference of genomic data. Leveraging lattice-based encryption schemes such as CRYSTALS-Kyber and Dilithium within a Hyperledger Fabric-based permissioned blockchain, the system guarantees resistance to quantum adversaries while maintaining confidentiality and auditability of genomic transactions. The architecture employs transformer-based genomic encoders for high-fidelity mutation detection and expression profiling, alongside hybrid data structures (Cuckoo filters and B+ trees) for fast, privacy-preserving variant querying. Smart contracts enforce an incentive-driven reward-penalty model, ensuring fair access while disincentivizing dishonest behavior. Empirical evaluations demonstrate a transaction throughput of 125 transactions per second, an average block formation time of 250 ms, and a post-quantum signature verification latency of 3.8 ms. The system achieved a genomic mutation detection accuracy of 97.31% and reduced query latency by up to 55% compared to existing methods. Additionally, it exhibited 1.75<span><math><mo>×</mo></math></span> faster access response times and 40% lower on-chain resource consumption by utilizing optimized off-chain storage and computation. Beyond theoretical design, our framework highlights practical integration pathways: (i) large post-quantum key sizes are managed through hybrid key encapsulation and off-chain storage, (ii) blockchain scalability issues are mitigated via optimized consensus and cross-network channeling, achieving high throughput and low latency, and (iii) quantum protocols such as QKD and Grover-accelerated search are incorporated in a modular fashion to address state preparation and storage challenges. This end-to-end quantum-secure framework offers a scalable, high-performance solution for collaborative genomic research, enabling compliance with emerging data privacy regulations while future-proofing against quantum-era threats.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104245"},"PeriodicalIF":3.7,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FM-DPDP: Fine-grained Multicopy Dynamic Provable Data Possession with flexible storage","authors":"Caiyuan Tang ,&nbsp;Feng Wang ,&nbsp;Chenbin Zhao ,&nbsp;Hui Cui ,&nbsp;Zuobin Ying ,&nbsp;Ching-Chun Chang ,&nbsp;Chin-Chen Chang","doi":"10.1016/j.jisa.2025.104262","DOIUrl":"10.1016/j.jisa.2025.104262","url":null,"abstract":"<div><div>In recent years, the widespread adoption of cloud storage has surged alongside the rapid and exponential increase in data. However, cloud storage also brings an integrity threat with remote data. Consequently, many schemes have been proposed to safeguard remote data integrity. In these researches, multicopy Provable data possession (PDP) is an effective method for enhancing data security. Unfortunately, most existing schemes only support integer multiples of copies, which may reduce the practicality of these schemes. In this paper, we use the Ternary Copy Hash Tree (TCHT) as a dynamic structure to propose a Fine-grained Multicopy Dynamic Provable Data Possession (FM-DPDP) scheme. Unlike traditional multicopy PDP, which only supports an integer number of copies, FM-DPDP supports fine-grained replication. This means that the number of replicas can be any rational number greater than 1. Furthermore, The FM-DPDP also supports, data restoration, privacy preservation and data dynamics. The security analysis demonstrates that the proposed scheme is correct and unforgeability. Besides, we compare FM-DPDP with other relevant schemes. The evaluation indicates that our scheme offers additional functionalities while maintaining comparable computational cost and communication efficiency to other schemes.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104262"},"PeriodicalIF":3.7,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ABA-LEP: Autonomous Bidirectional Authentication and Lightweight Encryption Protocol for drones under ARM architecture","authors":"Qian Zhou ,&nbsp;Jiayang Wu ,&nbsp;Weizhi Meng","doi":"10.1016/j.jisa.2025.104268","DOIUrl":"10.1016/j.jisa.2025.104268","url":null,"abstract":"<div><div>Secure communication protocols for drones are crucial in ensuring safety in potentially threatening network environments. However, existing protocols often suffer from weak autonomy, lack of optimization for ARM architecture, and inefficient utilization of lightweight cryptographic algorithms. To address these issues, this paper designs and analyzes an Autonomous Bidirectional Authentication and Lightweight Encryption Protocol (ABA-LEP) for drones under ARM architecture. The protocol optimizes the fixed-point scalar multiplication in SM2 for ARM architecture to accelerate authentication and key agreement efficiency, and employs simple operations like one-time pad limited XOR for lightweight secure communication encryption. Experiments conducted on the ARM Cortex M-4 based CrazyFlie 2.1 drone demonstrate that, in resource-constrained environments, the ABA-LEP achieves a performance improvement of up to 80.18% in fixed-point scalar multiplication with a 256-bit operand, compared to existing techniques. Additionally, the number of transmitted messages per unit time increases by up to 97.02%. The protocol’s resilience against multiple types of attacks has also been verified using the formal verification tool ProVerif.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"95 ","pages":"Article 104268"},"PeriodicalIF":3.7,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient U-shape invertible neural network for large-capacity image steganography","authors":"Le Zhang ,&nbsp;Tong Li ,&nbsp;Yao Lu ,&nbsp;Yuanrong Xu ,&nbsp;Guangming Lu","doi":"10.1016/j.jisa.2025.104237","DOIUrl":"10.1016/j.jisa.2025.104237","url":null,"abstract":"<div><div>Image steganography ensures covert communication by hiding secret information within cover images. The existing low-capacity steganography methods achieve satisfactory performances when hiding limited binary information within a cover image. However, it is still a challenge to recover high-quality revealed secret images from highly secure stego images with limited computational cost for large-capacity image steganography. This paper proposes an Efficient U-shape Invertible Neural Network (EUIN-Net) for large-capacity image steganography. Due to the gradual fusion and separation properties of the U-shape invertible mechanism, our EUIN-Net comprehensively fuses the secret and cover images on different scales and depths in the forward hiding process. Besides, the proposed EUIN-Net also maintains the independence of the cover and secret information in the backward revealing process. Moreover, the long-range dependency can be retrieved through using the skip connections between each pair U-shape invertible blocks. The above factors can drive our EUIN-Net to promote the quality of stego and revealed secret images. Furthermore, the shared and multi-scale characteristics of the U-shaped invertible blocks during the hiding and revealing stages contribute to significant reductions of our EUIN-Net in the model size, Flops, and GPU Memory occupancies. Extensive experiments demonstrate that the proposed EUIN-Net can achieve satisfactory performances with limited computational cost for large-capacity image steganography.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"94 ","pages":"Article 104237"},"PeriodicalIF":3.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A real-time automated attack-defense graph generation approach","authors":"Kéren A. Saint-Hilaire ,&nbsp;Christopher Neal ,&nbsp;Frédéric Cuppens ,&nbsp;Nora Boulahia-Cuppens ,&nbsp;Francesca Bassi ,&nbsp;Makhlouf Hadji","doi":"10.1016/j.jisa.2025.104266","DOIUrl":"10.1016/j.jisa.2025.104266","url":null,"abstract":"<div><div>With the increase in cyberattacks, developing appropriate strategies to mitigate and prevent them is essential. In the literature, tools exist that either help prevent or mitigate them. Attack graphs help define mitigation strategies because they help represent and visualize the attacker’s position on a system. However, the mitigation actions are not instantiated on the attack graph. This paper proposes an approach to generate an automated attack-defense graph based on real-time monitored system alerts and an extensive and comprehensive state-of-the-art review. We propose to enrich logical attack graphs generated by a logical reasoner. The enrichment process is possible thanks to a vulnerability ontology that infers additional impacts for an exploited vulnerability. We propose a countermeasure selection approach based on graph matching to generate an optimal Incident Response (IR) playbook. We propose instantiating the generated playbook’s IR actions to get an attack-defense graph in real-time. This instantiation is done thanks to anti-correlation. The anti-correlation ensures that the countermeasures are instantiated on the appropriate attack graph nodes. Only the IR actions whose execution can be launched automatically are applied. We validate our approach using two use-case scenarios that target critical industrial infrastructures. We analyze the countermeasures instantiated on the attack graphs for the scenarios that can achieve the attack goal. We evaluated the approach concerning the security relevance of instantiated countermeasures in attack graphs for several attack paths. The countermeasures instantiated on a node are always relevant to the attacker’s action represented by this node. We also evaluate the approach regarding time performance, considering several situations for the use-case scenarios. The generation time depends on the number of vulnerabilities involved in the scenario. The generation time is on average 0.161 s when the playbook has been generated before the attack defense graph generation process.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"94 ","pages":"Article 104266"},"PeriodicalIF":3.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HCCAS: A hierarchical consensus-based certificateless aggregate signcryption scheme for drone networks","authors":"Junfeng Tian,&nbsp;Zhengping Jiang,&nbsp;Yilun Jin","doi":"10.1016/j.jisa.2025.104260","DOIUrl":"10.1016/j.jisa.2025.104260","url":null,"abstract":"<div><div>Drone networks are dynamic and cooperative networks composed of multiple drones through wireless communication, which are widely used in search and rescue, patrol, and monitoring missions. Although multi-drone collaboration alleviates the limitations of individual drones in terms of computational and communication capabilities, large-scale deployments still face dual challenges of data security and communication efficiency. To address these issues, we propose a hierarchical consensus-based certificateless aggregate signcryption called the HCCAS scheme. Within each local airspace, a drone with superior computational and communication capabilities is elected as a local leader using the PBFT consensus algorithm. This local leader is responsible for aggregating the signcryption from all drones within its region. Subsequently, local leaders elect a global leader via the RAFT consensus algorithm, which transmits the total aggregated signcryption to the ground control station. In addition, HCCAS incorporates a pseudonym validity mechanism to provide conditional identity privacy protection. An efficient forgery localization mechanism based on a two-dimensional array is also designed, significantly reducing the computational overhead during verification. Compared with existing schemes, HCCAS achieves multiple security goals while reducing average computational cost and communication overhead by 52.09% and 39.69%, respectively. These results indicate enhanced adaptability and practicality in resource-constrained environments.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"94 ","pages":"Article 104260"},"PeriodicalIF":3.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cloud-assisted verifiable and traceable multi-party threshold private set intersection protocol for ride-sharing scheme","authors":"Qing Wu ,&nbsp;Xijia Dong ,&nbsp;Leyou Zhang ,&nbsp;Yue Lei ,&nbsp;Ziquan Zhao","doi":"10.1016/j.jisa.2025.104256","DOIUrl":"10.1016/j.jisa.2025.104256","url":null,"abstract":"<div><div>In recent years, ride-sharing services have experienced widespread adoption due to their ability to significantly reduce travel costs and carbon emissions. However, as the demand for ride-sharing increases, users are faced with growing challenges related to privacy protection and data security during the process of sharing transportation resources. In particular, there is a pressing need for effective solutions to mitigate the risks of the leakage of personally sensitive information and insufficient security verification. To address these challenges, a cloud-assisted, traceable ride-sharing scheme is proposed, leveraging a multi-party threshold private set intersection (MP-TPSI) protocol integrated with a digital signature verification mechanism. The proposed framework comprises an innovative MP-TPSI protocol, which includes two primary components: a multi-party cardinality testing phase that employs a secure comparison protocol (SCP) to determine if the intersection size surpasses a predefined threshold, coupled with a Proof of Ownership protocol to trace malicious users; and a PSI phase that securely computes the intersection using cloud-assisted computation. Furthermore, digital signature technology is incorporated to establish a robust verification framework, which ensures the authenticity of user identities and effectively mitigates the risks associated with ride mismatches and privacy leakage. Finally, comparative evaluation results demonstrate that the proposed MP-TPSI protocol achieves effective privacy protection with lower communication and computational overhead compared to existing schemes.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"94 ","pages":"Article 104256"},"PeriodicalIF":3.7,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time Automotive Ethernet Intrusion Detection Using Sliding Window-Based Temporal Convolutional Residual Attention Networks","authors":"Yuren Zhang,&nbsp;Jiapeng Xiu","doi":"10.1016/j.jisa.2025.104263","DOIUrl":"10.1016/j.jisa.2025.104263","url":null,"abstract":"<div><div>As vehicles become more intelligent and connected, automotive Ethernet is gradually replacing the traditional CAN bus as the backbone of in-vehicle networks. However, this transition introduces new security vulnerabilities. This paper presents a novel centralized network architecture and explores potential intrusion threats. Existing intrusion detection methods struggle to handle automotive Ethernet protocols universally and typically use a single machine learning network structure. Additionally, these models often fail to ensure temporal sensitivity and real-time performance. To address these issues, we propose Sliding Window-Based Temporal Convolutional Residual Attention Networks (SW-TempCRAN), a real-time intrusion detection system tailored for automotive Ethernet environments. SW-TempCRAN integrates several novel components, including protocol-general windowed feature extraction, time-aware positional encoding, convolutional residual attention networks and MLP classification with sequence-feature aggregation. It uses custom protocol-parsing scripts to extract key header and merge payload data, and combines Convolutional Neural Networks (CNN) with attention residual mechanisms. This design allows the model to capture attack patterns over time, identify local features and compress the computational load. We also improve positional encoding to better suit network traffic data, ensuring time sensitivity, while pre-generating the encoding matrix to avoid real-time computation complexity. Experiments on two public datasets show SW-TempCRAN outperforms state-of-the-art methods in evaluation metrics. SW-TempCRAN achieves F1-scores of 99.82 % and 98.05 % on two datasets, with a detection delay of less than 1.5 ms on a server testbed.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"94 ","pages":"Article 104263"},"PeriodicalIF":3.7,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sym-CS-HFL: A secure and efficient solution for privacy-preserving heterogeneous federated learning","authors":"Jinzhao Wang ,&nbsp;Wenlong Tian ,&nbsp;Junwei Tang ,&nbsp;Xuming Ye ,&nbsp;Yaping Wan ,&nbsp;Zhiyong Xu ,&nbsp;Lingna Chen","doi":"10.1016/j.jisa.2025.104253","DOIUrl":"10.1016/j.jisa.2025.104253","url":null,"abstract":"<div><div>In the era of big data, deep learning models play a crucial role in identifying underlying patterns within data. However, the need for large volumes of training data, often scattered across various organizations with privacy constraints, poses a significant challenge. Federated Learning (FL) addresses this by enabling the collaborative training of models without sharing the underlying data. Despite its promise, FL encounters challenges with model privacy leakage and computational overhead, particularly when dealing with non-identically distributed (Non-IID) data. To overcome these challenges, we introduce Sym-CS-HFL, a novel Privacy-Preserving Federated Learning (PPFL) framework that combines Symmetric Homomorphic Encryption with a Local Adaptive Aggregation (LAA) scheme. Our approach minimizes the reliance on asymmetric keys, simplifying the encryption process and reducing computational overhead. We implement a DCT-Neural Network Compressive Sensing Scheme to decrease communication costs substantially. Furthermore, the LAA scheme addresses the heterogeneity in Non-IID data, enhancing model convergence and accuracy. Our experiments on diverse datasets, including MNIST, FashionMNIST, CIFAR-10/100, and AG News, demonstrate that Sym-CS-HFL achieves a Top-3 test accuracy while significantly reducing communication overhead by <span><math><mrow><mn>15</mn><mo>.</mo><mn>2</mn><mo>×</mo></mrow></math></span> to <span><math><mrow><mn>74</mn><mo>×</mo></mrow></math></span> compared to existing HE schemes. The computational overhead is also reduced, with training times only <span><math><mrow><mn>1</mn><mo>.</mo><mn>1</mn><mo>×</mo></mrow></math></span> to <span><math><mrow><mn>1</mn><mo>.</mo><mn>8</mn><mo>×</mo></mrow></math></span> that of plaintext training. These results underscore Sym-CS-HFL’s effectiveness in maintaining high performance and privacy in PPFL.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"94 ","pages":"Article 104253"},"PeriodicalIF":3.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}