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  • Open Access

    REVIEW

    Ensuring User Privacy and Model Security via Machine Unlearning: A Review

    Yonghao Tang1, Zhiping Cai1,*, Qiang Liu1, Tongqing Zhou1, Qiang Ni2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2645-2656, 2023, DOI:10.32604/cmc.2023.032307

    Abstract As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy. Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The data provider hopes that the model trainer can prove to them the confidentiality of the model. Trainer will be required to withdraw data when the trust collapses. In the meantime, trainers hope to forget the injected data More >

  • Open Access

    ARTICLE

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

    Sanxiu Jiao1, Lecai Cai2,*, Xinjie Wang1, Kui Cheng2, Xiang Gao3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1679-1694, 2024, DOI:10.32604/cmes.2023.030512

    Abstract As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection… More > Graphic Abstract

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

  • Open Access

    ARTICLE

    Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

    Abdulatif Alabdulatif1,*, Mada Alharbi1, Abir Mchergui2, Tarek Moulahi3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 2005-2022, 2024, DOI:10.32604/cmes.2023.029769

    Abstract The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency. Revolutionary advanced technology, such as Intelligent Transportation Systems (ITS), enables improved traffic management, helps eliminate congestion, and supports a safer environment. ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users. However, ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks (VANETs). This is because each vehicle plays the role of a router in this… More >

  • Open Access

    REVIEW

    A Survey on Sensor- and Communication-Based Issues of Autonomous UAVs

    Pavlo Mykytyn1,2,*, Marcin Brzozowski1, Zoya Dyka1,2, Peter Langendoerfer1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1019-1050, 2024, DOI:10.32604/cmes.2023.029075

    Abstract The application field for Unmanned Aerial Vehicle (UAV) technology and its adoption rate have been increasing steadily in the past years. Decreasing cost of commercial drones has enabled their use at a scale broader than ever before. However, increasing the complexity of UAVs and decreasing the cost, both contribute to a lack of implemented security measures and raise new security and safety concerns. For instance, the issue of implausible or tampered UAV sensor measurements is barely addressed in the current research literature and thus, requires more attention from the research community. The goal of this… More >

  • Open Access

    ARTICLE

    A Conditionally Anonymous Linkable Ring Signature for Blockchain Privacy Protection

    Quan Zhou1,*, Yulong Zheng1, Minhui Chen2, Kaijun Wei2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2851-2867, 2023, DOI:10.32604/csse.2023.039908

    Abstract In recent years, the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention. To ensure privacy protection for both sides of the transaction, many researchers are using ring signature technology instead of the original signature technology. However, in practice, identifying the signer of an illegal blockchain transaction once it has been placed on the chain necessitates a signature technique that offers conditional anonymity. Some illegals can conduct illegal transactions and evade the law using ring signatures, which offer perfect anonymity. This paper firstly constructs a conditionally anonymous linkable ring… More >

  • Open Access

    ARTICLE

    Detecting Phishing Using a Multi-Layered Social Engineering Framework

    Kofi Sarpong Adu-Manu*, Richard Kwasi Ahiable

    Journal of Cyber Security, Vol.5, pp. 13-32, 2023, DOI:10.32604/jcs.2023.043359

    Abstract As businesses develop and expand with a significant volume of data, data protection and privacy become increasingly important. Research has shown a tremendous increase in phishing activities during and after COVID-19. This research aimed to improve the existing approaches to detecting phishing activities on the internet. We designed a multi-layered phish detection algorithm to detect and prevent phishing applications on the internet using URLs. In the algorithm, we considered technical dimensions of phishing attack prevention and mitigation on the internet. In our approach, we merge, Phishtank, Blacklist, Blocklist, and Whitelist to form our framework. A More >

  • Open Access

    ARTICLE

    A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

    Muhammad Firdaus1, Harashta Tatimma Larasati2, Kyung-Hyune Rhee3,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2959-2978, 2023, DOI:10.32604/cmc.2023.039487

    Abstract The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks… More >

  • Open Access

    ARTICLE

    A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

    Burak Cem Kara1,2,*, Can Eyüpoğlu1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1515-1535, 2023, DOI:10.32604/cmc.2023.040274

    Abstract Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, k-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a… More >

  • Open Access

    ARTICLE

    Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology

    Sur Singh Rawat1,*, Youseef Alotaibi2, Nitima Malsa1, Vimal Gupta1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1797-1815, 2023, DOI:10.32604/cmc.2023.039381

    Abstract Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone… More >

  • Open Access

    ARTICLE

    Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624

    Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based More >

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