International Journal of Network Management最新文献

{"title":"Homomorphic Witness Encryption and Its Applications","authors":"Yuzhu Wang, Xingbo Wang, Mingwu Zhang","doi":"10.1002/nem.2303","DOIUrl":"https://doi.org/10.1002/nem.2303","url":null,"abstract":"<div>\u0000 \u0000 <p>In witness encryption (<span>WE</span>), an instance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>x</mi>\u0000 </mrow>\u0000 <annotation>$$ x $$</annotation>\u0000 </semantics></math> of an <span>NP</span> problem is allowed to be used to encrypt a message, and who holding a witness of the problem can efficiently decrypt the ciphertext. In this work, we put forth the concept of homomorphic witness encryption (<span>HWE</span>), where one can evaluate functions over ciphertexts of the same instance without decrypting them, that is, one can manipulate a set of ciphertexts with messages <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 <mo>,</mo>\u0000 <mo>⋯</mo>\u0000 <mspace></mspace>\u0000 <mo>,</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left({M}_1,cdots, {M}_nright) $$</annotation>\u0000 </semantics></math> to obtain the evaluation of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>f</mi>\u0000 <mo>(</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 <mo>,</mo>\u0000 <mo>⋯</mo>\u0000 <mspace></mspace>\u0000 <mo>,</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ fleft({M}_1,cdots, {M}_nright) $$</annotation>\u0000 </semantics></math>, for any function <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>f</mi>\u0000 </mrow>\u0000 <annotation>$$ f $$</annotation>\u0000 </semantics></math>. We declare that such homomorphic witness encryption schemes can be generically constructed from indistinguishable obfuscation (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>i</mi>\u0000 <mi>O</mi>\u0000 </mrow>\u0000 <annotation>$$ imathcal{O} $$</annotation>\u0000 ","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Risk-Aware SDN Defense Framework Against Anti-Honeypot Attacks Using Safe Reinforcement Learning","authors":"Dongying Gao,&nbsp;Caiwei Guo,&nbsp;Yi Zhang,&nbsp;Wen Ji,&nbsp;Zhilei Lv,&nbsp;Zheng Li,&nbsp;Kunsan Zhang,&nbsp;Ruibin Lin","doi":"10.1002/nem.2297","DOIUrl":"10.1002/nem.2297","url":null,"abstract":"<div>\u0000 \u0000 <p>The development of multiple attack methods by external attackers in recent years poses a huge challenge to the security and efficient operation of software-defined networks (SDN), which are the core of operational controllers and data storage. Therefore, it is critical to ensure that the normal process of network interaction between SDN servers and users is protected from external attacks. In this paper, we propose a risk-aware SDN defense framework based on safe reinforcement learning (SRL) to counter multiple attack actions. Specifically, the defender uses SRL to maximize the utility by choosing to provide a honeypot service or pseudo-honeypot service within predefined security constraints, while the external attacker maximizes the utility by choosing an anti-honeypot attack or masquerade attack. To describe the system risk in detail, we introduce the risk level function to model the simultaneous dynamic attack and defense processes. Simulation results demonstrate that our proposed risk-aware scheme improves the defense utility by 17.5% and 142.4% and reduces the system risk by 42.7% and 59.6% compared to the QLearning scheme and the Random scheme, respectively.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142247756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Editorial for the IJNM Special Issue From the Best Papers of IEEE ICBC 2023 “Advancing Blockchain and Cryptocurrency”","authors":"Laura Ricci,&nbsp;Moayad Aloqaily,&nbsp;Vinayaka Pandit","doi":"10.1002/nem.2301","DOIUrl":"10.1002/nem.2301","url":null,"abstract":"<p>This special issue contains extended versions of the best papers from 2023 IEEE International Conference on Blockchain and Cryptocurrency. The conference was held from May 1 to May 5, 2023, in Dubai, UAE. The papers in this special issue explore crucial advancements in illicit activity tracking, transaction mechanisms, synchronization, and database integration. The following papers highlight critical advancements and address complex challenges in these domains.</p><p>The first paper, “The next phase of identifying illicit activity in Bitcoin” by Jack Nicholls and his team, deepens the discourse on securing Bitcoin transactions. By analyzing current methods and proposing enhancements through machine learning, this paper provides crucial insights into improving the detection of illicit activities and enhancing network security.</p><p>In the second paper, “Transaction fee mechanisms with farsighted miners,” authored by Jens Leth Hougaard and colleagues, strategic miner behaviors in the Ethereum network are explored under the new fee mechanism, EIP1559. The paper extends the discussion to strategic foresight in mining operations, presenting a model that evaluates the impacts of varying degrees of hashing power and foresight on network throughput and block variability.</p><p>The third contribution, “Out-of-band transaction pool sync for large dynamic blockchain networks” by Novak Boskov et al., innovates the synchronization of transaction pools across large and dynamic blockchain networks. Employing the novel SREP algorithm, this study provides a comprehensive approach with proven scalability and performance improvements, particularly emphasizing reduced block propagation delays and bandwidth overhead.</p><p>The fourth paper, “DELTA: A Modular, Transparent and Efficient Synchronization of DLTs and Databases” by Fernández-Bravo Peñuela et al., addresses the integration of blockchain data into traditional databases. The DELTA system offers a seamless, efficient solution for querying blockchain data within enterprise systems, proving significantly faster and more reliable than existing methods.</p><p>These papers collectively enhance our understanding of blockchain technology's application, offering new methodologies, insights into miner behavior, security enhancements, and integration techniques for enterprise systems. Their contributions are instrumental in paving the way for more robust, efficient, and secure blockchain networks.</p><p>We are immensely grateful to the authors for their innovative research, the reviewers for their critical insights, and the editorial team for their commitment to compiling this transformative special issue.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Duo-H: An Effectual Consensus Algorithm Using Two-Tier Shard Consortium Blockchain Mechanism for Enhanced Privacy Protection","authors":"C. H. Sarada Devi,&nbsp;R. Anand,&nbsp;R. Hemalatha,&nbsp;B. Uma Maheswari","doi":"10.1002/nem.2300","DOIUrl":"10.1002/nem.2300","url":null,"abstract":"<div>\u0000 \u0000 <p>Blockchain is an innovative technology for storing data in decentralized, distributed, and secure chain blocks. Consortium blockchains are commonly used in transactions where transactions between organizations are also achieved by the blockchain. In the classic consortium blockchain system, entire nodes are added to each other in the process of transaction consensus. This leads to lower confidentiality in protecting transaction data within the organizations in the consortium. The throughput of the existing consortium blockchain system is still low. To solve the above problems, the paper proposes a two-tier consortium blockchain with transaction privacy based on sharding technology. First, a trust value assessment is carried out to select the nodes of the blockchain. The duo-head observation strategy uses these trust values to identify the nonmalicious node. Finally, the consensus separation approach based on the guarantee mechanism strategy with the shard nodes is presented. This approach is used to select reliable nodes for the blocks to be stored. The proposed consortium blockchain approach evaluation is done in terms of latency, throughput, and transactions per second metrics. As a result of the evaluations, the proposed model with 32 shards possesses 143,891 \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>tx</mi>\u0000 <mo>/</mo>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation>$$ tx/s $$</annotation>\u0000 </semantics></math> of throughput and 1.11 s of latency. Moreover, by the proposed two-tier consortium model, time consumption is also decreased when uploading data. For a data set of 50,000, the suggested model has a time consumption of 96 s. The proposed research results in higher throughput and less latency in transactions. Also, the research enhances the scalability and reliability by overcoming the poor node issues.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Intelligent and Trust-Enabled Farming Systems With Blockchain and Digital Twins on Mobile Edge Computing","authors":"Geetanjali Rathee,&nbsp;Hemraj Saini,&nbsp;Selvaraj Praveen Chakkravarthy,&nbsp;Rajagopal Maheswar","doi":"10.1002/nem.2299","DOIUrl":"10.1002/nem.2299","url":null,"abstract":"<div>\u0000 \u0000 <p>Advancement and flourishment in mobile edge computing (MEC) have motivated the farmers to deploy an efficient ecosystem in their farms. For further real-time monitoring and surveillance of the environment along with the deployment of intelligent farming, digital twin is considered as one of the emerging and most promising technologies. For proper optimization and utilization of physical systems, the physical components of the ecosystems are connected with the digital space. Further, the smart technologies and devices have convinced to address the expected level of requirements for accessing the rapid growth in farming associated with digital twins. However, with a large number of smart devices, huge amount of generated information from heterogeneous devices may increase the privacy and security concern by challenging the interrupting operations and management of services in smart farming. In addition, the growing risks associated with MEC by modifying the sensor readings and quality of service further affect the overall growth of intelligent farming. In order to resolve these challenges, this paper has proposed a secure surveillance architecture to detect deviations by incorporating digital twins in the ecosystem. Further, for real-time monitoring and preprocessing of information, we have integrated a four-dimensional trust mechanism along with blockchain. The four-dimensional trusted method recognizes the behavior of each communicating device during the transmission of information in the network. Further, blockchain strengthens the surveillance process of each device behavior by continuously monitoring their activities. The proposed mechanism is tested and verified against various abnormalities received from sensors by simulating false use cases in the ecosystem and compared against various security metrics over existing approaches. Furthermore, the proposed mechanism is validated against several security threats such as control command threat, coordinated cyber threats, accuracy, and decision-making and prediction of records over existing methods.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"ProKube: Proactive Kubernetes Orchestrator for Inference in Heterogeneous Edge Computing","authors":"Babar Ali,&nbsp;Muhammed Golec,&nbsp;Sukhpal Singh Gill,&nbsp;Felix Cuadrado,&nbsp;Steve Uhlig","doi":"10.1002/nem.2298","DOIUrl":"10.1002/nem.2298","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep neural network (DNN) and machine learning (ML) models/ inferences produce highly accurate results demanding enormous computational resources. The limited capacity of end-user smart gadgets drives companies to exploit computational resources in an edge-to-cloud continuum and host applications at user-facing locations with users requiring fast responses. Kubernetes hosted inferences with poor resource request estimation results in service level agreement (SLA) violation in terms of latency and below par performance with higher end-to-end (E2E) delays. Lifetime static resource provisioning either hurts user experience for under-resource provisioning or incurs cost with over-provisioning. Dynamic scaling offers to remedy delay by upscaling leading to additional cost whereas a simple migration to another location offering latency in SLA bounds can reduce delay and minimize cost. To address this cost and delay challenges for ML inferences in the inherent heterogeneous, resource-constrained, and distributed edge environment, we propose ProKube, which is a proactive container scaling and migration orchestrator to dynamically adjust the resources and container locations with a fair balance between cost and delay. ProKube is developed in conjunction with Google Kubernetes Engine (GKE) enabling cross-cluster migration and/ or dynamic scaling. It further supports the regular addition of freshly collected logs into scheduling decisions to handle unpredictable network behavior. Experiments conducted in heterogeneous edge settings show the efficacy of ProKube to its counterparts cost greedy (CG), latency greedy (LG), and GeKube (GK). ProKube offers 68%, 7%, and 64% SLA violation reduction to CG, LG, and GK, respectively, and it improves cost by 4.77 cores to LG and offers more cost of 3.94 to CG and GK.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An IoT Intrusion Detection Approach Based on Salp Swarm and Artificial Neural Network","authors":"Omar A. Alzubi,&nbsp;Jafar A. Alzubi,&nbsp;Issa Qiqieh,&nbsp;Ala' M. Al-Zoubi","doi":"10.1002/nem.2296","DOIUrl":"10.1002/nem.2296","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things has emerged as a significant and influential technology in modern times. IoT presents solutions to reduce the need for human intervention and emphasizes task automation. According to a Cisco report, there were over 14.7 billion IoT devices in 2023. However, as the number of devices and users utilizing this technology grows, so does the potential for security breaches and intrusions. For instance, insecure IoT devices, such as smart home appliances or industrial sensors, can be vulnerable to hacking attempts. Hackers might exploit these vulnerabilities to gain unauthorized access to sensitive data or even control the devices remotely. To address and prevent this issue, this work proposes integrating intrusion detection systems (IDSs) with an artificial neural network (ANN) and a salp swarm algorithm (SSA) to enhance intrusion detection in an IoT environment. The SSA functions as an optimization algorithm that selects optimal networks for the multilayer perceptron (MLP). The proposed approach has been evaluated using three novel benchmarks: Edge-IIoTset, WUSTL-IIOT-2021, and IoTID20. Additionally, various experiments have been conducted to assess the effectiveness of the proposed approach. Additionally, a comparison is made between the proposed approach and several approaches from the literature, particularly SVM combined with various metaheuristic algorithms. Then, identify the most crucial features for each dataset to improve detection performance. The SSA-MLP outperforms the other algorithms with 88.241%, 93.610%, and 97.698% for Edge-IIoTset, IoTID20, and WUSTL, respectively.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Intrusion Detection for Blockchain-Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model","authors":"C. U. Om Kumar,&nbsp;Suguna Marappan,&nbsp;Bhavadharini Murugeshan,&nbsp;P. Mercy Rajaselvi Beaulah","doi":"10.1002/nem.2295","DOIUrl":"10.1002/nem.2295","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain-based deep learning model for IDS. Initially, the input data are preprocessed using min-max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM-FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM-FCVAE model is analyzed by evaluating several metrics. The proposed GM-FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW-NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM-FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks","authors":"Muhammad Yousaf Saeed,&nbsp;Jingsha He,&nbsp;Nafei Zhu,&nbsp;Muhammad Farhan,&nbsp;Soumyabrata Dev,&nbsp;Thippa Reddy Gadekallu,&nbsp;Ahmad Almadhor","doi":"10.1002/nem.2294","DOIUrl":"10.1002/nem.2294","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Blockchain-Enabled Decentralized Healthcare Data Exchange: Leveraging Novel Encryption Scheme, Smart Contracts, and Ring Signatures for Enhanced Data Security and Patient Privacy","authors":"S. Vidhya,&nbsp;P. M. Siva Raja,&nbsp;R. P. Sumithra","doi":"10.1002/nem.2289","DOIUrl":"10.1002/nem.2289","url":null,"abstract":"<div>\u0000 \u0000 <p>The healthcare industry has undergone a digital transformation in recent years, with the adoption of electronic health records (EHRs) becoming increasingly prevalent. While this digitization offers various advantages, concerns regarding the security and privacy of sensitive medical data have also intensified. Data breaches and cyber-attacks targeting healthcare organizations have underscored the need for robust solutions to protect patient data. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature, which ensures secure and transparent data recording. This paper proposes a novel approach that combines blockchain with advanced encryption scheme and privacy protection technique to establish a secure and privacy protected medical data sharing environment. The proposed system consists of three phases such as initialization phase, data processing phase, and authentication phase. The hybrid Feistal-Shannon homomorphic encryption algorithm (HFSHE) is proposed to encrypt the medical data to ensure data confidentiality, integrity, and availability. Ring signature is integrated to the system to provide additional anonymity and protect the identities of the participants involved in data transactions. In addition, the smart contract developed performs authentication checks on users, generates a time seal, and verifies the ring signature. Through this enhancement, the system becomes more resilient to both external and internal threats, enhancing overall security as well as privacy. A comprehensive security analysis is conducted to compare the proposed method's performance against existing techniques. The results demonstrate the effectiveness of the proposed approach in safeguarding sensitive medical information within the blockchain ecosystem.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}