High-Confidence Computing最新文献_第5页

An improved secure designated server certificateless authenticated searchable encryption scheme for IIoT 一种改进的工业物联网安全指定服务器无证书认证可搜索加密方案

IF 3

High-Confidence Computing Pub Date : 2025-09-01 Epub Date: 2025-02-12 DOI: 10.1016/j.hcc.2025.100301

Le Zhang , Feng Zhou , Qijia Zhang , Wei Xiong , Youliang Tian

{"title":"An improved secure designated server certificateless authenticated searchable encryption scheme for IIoT","authors":"Le Zhang , Feng Zhou , Qijia Zhang , Wei Xiong , Youliang Tian","doi":"10.1016/j.hcc.2025.100301","DOIUrl":"10.1016/j.hcc.2025.100301","url":null,"abstract":"<div><div>The Industrial Internet of Things (IIoT) achieves the automation, monitoring, and optimization of industrial processes by interconnecting various sensors, smart devices, and the Internet, which dramatically increases productivity and product quality. Nevertheless, the IIoT comprises a substantial amount of sensitive data, which requires encryption to ensure data privacy and security. Recently, Sun et al. proposed a certificateless searchable encryption scheme for IIoT to enable the retrieval of ciphertext data while protecting data privacy. However, we found that their scheme not only fails to satisfy trapdoor indistinguishability but also lacks defense against keyword guessing attacks. In addition, some schemes use deterministic algorithms in the encryption process, resulting in the same ciphertexts after encryption for the same keyword, thereby leaking the potential frequency distribution of the keyword in the ciphertext space, thereby leaking the potential frequency distribution of the keyword in the ciphertext space, allowing attackers to infer the plaintext information corresponding to the ciphertext through statistical analysis. To better protect data privacy, we propose an improved certificateless searchable encryption scheme with a designated server. With security analysis, we prove that our scheme provides multi-ciphertext indistinguishability and multi-trapdoor indistinguishability security under the random oracle. Experimental results show that the proposed scheme has good overall performance in terms of computational overhead, communication overhead, and security features.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100301"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain FedViTBloc：使用联合视觉变压器和区块链的安全和隐私增强的医学图像分析

IF 3

High-Confidence Computing Pub Date : 2025-09-01 Epub Date: 2025-02-15 DOI: 10.1016/j.hcc.2025.100302

Gabriel Chukwunonso Amaizu , Akshita Maradapu Vera Venkata Sai , Sanjay Bhardwaj , Dong-Seong Kim , Madhuri Siddula , Yingshu Li

{"title":"FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain","authors":"Gabriel Chukwunonso Amaizu , Akshita Maradapu Vera Venkata Sai , Sanjay Bhardwaj , Dong-Seong Kim , Madhuri Siddula , Yingshu Li","doi":"10.1016/j.hcc.2025.100302","DOIUrl":"10.1016/j.hcc.2025.100302","url":null,"abstract":"<div><div>The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100302"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-based cooperative content caching and sharing for multi-layer vehicular networks 基于学习的多层车辆网络协同内容缓存与共享

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-11-05 DOI: 10.1016/j.hcc.2024.100277

Jun Shi , Yuanzhi Ni , Lin Cai , Zhuocheng Du

{"title":"Learning-based cooperative content caching and sharing for multi-layer vehicular networks","authors":"Jun Shi , Yuanzhi Ni , Lin Cai , Zhuocheng Du","doi":"10.1016/j.hcc.2024.100277","DOIUrl":"10.1016/j.hcc.2024.100277","url":null,"abstract":"<div><div>Caching and sharing the content files are critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100277"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs 最小化WRSN在多muv和激光充电无人机辅助下的充电任务时间

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-10-05 DOI: 10.1016/j.hcc.2024.100272

Jian Zhang , Chuanwen Luo , Ning Liu , Yi Hong , Zhibo Chen

{"title":"Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs","authors":"Jian Zhang , Chuanwen Luo , Ning Liu , Yi Hong , Zhibo Chen","doi":"10.1016/j.hcc.2024.100272","DOIUrl":"10.1016/j.hcc.2024.100272","url":null,"abstract":"<div><div>This paper investigates the framework of wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs) and laser-charged unmanned aerial vehicles (UAVs). On the basis of framework, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the time consumption of UAV that takes the most time of all UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the multiple UAVs-based TSP (MUTSP) problem to balance the charging tasks assigned to every UAV. Then, based on the MUTSP problem, we propose the TOUM algorithm (TOUMA) to design the detailed travel plan of UAVs and MUVs. We also present an algorithm named TOUM-DQN to make intelligent decisions about the travel plan of UAVs and MUVs by extracting valuable information from the network. The effectiveness of proposed algorithms is verified through extensive simulation experiments. The results demonstrate that the TOUMA algorithm outperforms the solar charging method, the base station charging method, and the TOUM-DQN algorithm in terms of time efficiency. Simultaneously, the experimental results show that the execution time of TOUM-DQN algorithm is significantly lower than TOUMA algorithm.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100272"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain-based inter-operator settlement system 基于区块链的运营商间结算系统

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2025-04-30 DOI: 10.1016/j.hcc.2025.100324

Shifu Zhang, Yulin Pan

{"title":"Blockchain-based inter-operator settlement system","authors":"Shifu Zhang, Yulin Pan","doi":"10.1016/j.hcc.2025.100324","DOIUrl":"10.1016/j.hcc.2025.100324","url":null,"abstract":"<div><div>Inter-network settlement is a critical mechanism for ensuring quality service and sustainable growth in the telecommunications industry. However, existing practices among operators suffer from inefficient, including manual workflows, untrustworthy data foundations, insecure dispute resolution, and insufficient accountability oversight. These challenges lead to prolonged settlement cycles, operational redundancies, and heightened risks of errors or leaks. To address these issues, we propose a blockchain-powered settlement chain framework that integrates business and technical systems to enable intelligent, trusted, and automated cross-operator settlement management. By synergizing consortium blockchain, privacy-preserving computation, and decentralized governance protocols, the framework establishes an end-to-end digital workflow covering data exchange, verification, auditing, and reconciliation. Key innovations include: (1) a multi-operator co-built consortium chain with cross-cloud networking and peer-to-peer governance; (2) a “data-available-but-invisible” auditing mechanism combining blockchain and privacy-preserving computation to ensure secure, compliant interactions; and (3) a dynamic chaincode architecture supporting real-time rule synchronization and adaptive cryptographic controls. The framework achieves full-process traceability, automated reconciliation, and enhanced financial governance while reducing reliance on manual intervention. This work provides a transformative paradigm for modernizing telecommunications settlement systems through digital trust infrastructure.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100324"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel deep high-level concept-mining jointing hashing model for unsupervised cross-modal retrieval 一种新的用于无监督跨模态检索的深度高级概念挖掘连接哈希模型

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-10-29 DOI: 10.1016/j.hcc.2024.100274

Chun-Ru Dong , Jun-Yan Zhang , Feng Zhang , Qiang Hua , Dachuan Xu

{"title":"A novel deep high-level concept-mining jointing hashing model for unsupervised cross-modal retrieval","authors":"Chun-Ru Dong , Jun-Yan Zhang , Feng Zhang , Qiang Hua , Dachuan Xu","doi":"10.1016/j.hcc.2024.100274","DOIUrl":"10.1016/j.hcc.2024.100274","url":null,"abstract":"<div><div>Unsupervised cross-modal hashing has achieved great success in various information retrieval applications owing to its efficient storage usage and fast retrieval speed. Recent studies have primarily focused on training the hash-encoded networks by calculating a sample-based similarity matrix to improve the retrieval performance. However, there are two issues remain to solve: (1) The current sample-based similarity matrix only considers the similarity between image-text pairs, ignoring the different information densities of each modality, which may introduce additional noise and fail to mine key information for retrieval; (2) Most existing unsupervised cross-modal hashing methods only consider alignment between different modalities, while ignoring consistency between each modality, resulting in semantic conflicts. To tackle these challenges, a novel Deep High-level Concept-mining Jointing Hashing (DHCJH) model for unsupervised cross-modal retrieval is proposed in this study. DHCJH is able to capture the essential high-level semantic information from image modalities and integrate into the text modalities to improve the accuracy of guidance information. Additionally, a new hashing loss with a regularization term is introduced to avoid the cross-modal semantic collision and false positive pairs problems. To validate the proposed method, extensive comparison experiments on benchmark datasets are conducted. Experimental findings reveal that DHCJH achieves superior performance in both accuracy and efficiency. The code of DHCJH is available at Github.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100274"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates 基于运行时验证和需求更新的智能配电系统动态评估方法

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-07-09 DOI: 10.1016/j.hcc.2024.100255

Yunshuo Li , Xiangjun Duan , Yuanyuan Xu , Cheng Zhao

{"title":"Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates","authors":"Yunshuo Li , Xiangjun Duan , Yuanyuan Xu , Cheng Zhao","doi":"10.1016/j.hcc.2024.100255","DOIUrl":"10.1016/j.hcc.2024.100255","url":null,"abstract":"<div><div>The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100255"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An overview of machine unlearning 机器非学习概述

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-07-06 DOI: 10.1016/j.hcc.2024.100254

Chunxiao Li , Haipeng Jiang , Jiankang Chen , Yu Zhao , Shuxuan Fu , Fangming Jing , Yu Guo

引用次数: 0

A study on an efficient OSS inspection scheme based on encrypted GML 基于加密GML的OSS检测方案研究

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-11-05 DOI: 10.1016/j.hcc.2024.100279

Seok-Joon Jang , Im-Yeong Lee , Daehee Seo , Su-Hyun Kim

{"title":"A study on an efficient OSS inspection scheme based on encrypted GML","authors":"Seok-Joon Jang , Im-Yeong Lee , Daehee Seo , Su-Hyun Kim","doi":"10.1016/j.hcc.2024.100279","DOIUrl":"10.1016/j.hcc.2024.100279","url":null,"abstract":"<div><div>The importance of Open Source Software (OSS) has increased in recent years. OSS is software that is jointly developed and maintained globally through open collaboration and knowledge sharing. OSS plays an important role, especially in the Information Technology (IT) field, by increasing the efficiency of software development and reducing costs. However, licensing issues, security issues, etc., may arise when using OSS. Some services analyze source code and provide OSS-related data to solve these problems, a representative example being Blackduck. Blackduck inspects the entiresource code within the project and provides OSS information and related data included in the whole project. Therefore, there are problems such as inefficiency due to full inspection of the source code and difficulty in determining the exact location where OSS is identified. This paper proposes a scheme to intuitively analyze source code through Graph Modelling Language (GML) conversion to solve these problems. Additionally, encryption is applied to GML to performsecure GML-based OSS inspection. The study explains the process of converting source code to GML and performing OSS inspection. Afterward, we compare the capacity and accuracy of text-based OSS inspection and GML-based OSS inspection. Signcryption is applied to performsafe, GML-based, efficient OSS inspection.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100279"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization IDL-LTSOJ：基于深度神经网络的缺陷定位智能在线判断系统的研究与实现

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-11-01 DOI: 10.1016/j.hcc.2024.100268

Lihua Song , Ying Han , Yufei Guo , Chenying Cai

{"title":"IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization","authors":"Lihua Song , Ying Han , Yufei Guo , Chenying Cai","doi":"10.1016/j.hcc.2024.100268","DOIUrl":"10.1016/j.hcc.2024.100268","url":null,"abstract":"<div><div>The evolution of artificial intelligence has thrust the Online Judge (OJ) systems into the forefront of research, particularly within programming education, with a focus on enhancing performance and efficiency. Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture, this paper introduces an innovative Integrated Intelligent Defect Localization and Lightweight Task Scheduling Online Judge (IDL-LTSOJ) system. Firstly, to achieve token-level fine-grained defect localization, a Deep Fine-Grained Defect Localization (Deep-FGDL) deep neural network model is developed. By integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), this model extracts fine-grained information from the abstract syntax tree (AST) of code, enabling more accurate defect localization. Subsequently, we propose a lightweight task scheduling architecture to tackle issues, such as limited concurrency in task evaluation and high equipment costs. This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks, substantially enhancing system evaluation efficiency. The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9% in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks. Moreover, the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes, which represents a significant improvement in evaluation efficiency over centralized evaluation methods.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100268"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0