Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (217)
  • Open Access

    ARTICLE

    Blockchain-Based Certificateless Bidirectional Authenticated Searchable Encryption Scheme in Cloud Email System

    Yanzhong Sun1, Xiaoni Du1,*, Shufen Niu2, Xiaodong Yang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3287-3310, 2024, DOI:10.32604/cmes.2023.043589

    Abstract Traditional email systems can only achieve one-way communication, which means only the receiver is allowed to search for emails on the email server. In this paper, we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption (CL-BSE) by combining the storage function of cloud server with the communication function of email server. In the new model, not only can the data receiver search for the relevant content by generating its own trapdoor, but the data owner also can retrieve the content in the same… More >

  • Open Access

    REVIEW

    A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

    Yuming Tang1,#, Yitian Zhang2,#, Tao Niu1, Zhen Li2,3,*, Zijian Zhang1,3, Huaping Chen4, Long Zhang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2451-2477, 2024, DOI:10.32604/cmes.2024.030084

    Abstract Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data… More >

  • Open Access

    REVIEW

    A Comprehensive Survey for Privacy-Preserving Biometrics: Recent Approaches, Challenges, and Future Directions

    Shahriar Md Arman1, Tao Yang1,*, Shahadat Shahed2, Alanoud Al Mazroa3, Afraa Attiah4, Linda Mohaisen4

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2087-2110, 2024, DOI:10.32604/cmc.2024.047870

    Abstract The rapid growth of smart technologies and services has intensified the challenges surrounding identity authentication techniques. Biometric credentials are increasingly being used for verification due to their advantages over traditional methods, making it crucial to safeguard the privacy of people’s biometric data in various scenarios. This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems. It proposes a noble and thorough taxonomy survey for privacy-preserving techniques, as well as a systematic framework for categorizing the field’s existing literature. We review the state-of-the-art methods and address their advantages and limitations in More >

  • Open Access

    REVIEW

    A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

    Sunil Kumar1, Dilip Kumar1, Ramraj Dangi2, Gaurav Choudhary3, Nicola Dragoni4, Ilsun You5,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 31-63, 2024, DOI:10.32604/cmc.2023.047084

    Abstract The widespread and growing interest in the Internet of Things (IoT) may be attributed to its usefulness in many different fields. Physical settings are probed for data, which is then transferred via linked networks. There are several hurdles to overcome when putting IoT into practice, from managing server infrastructure to coordinating the use of tiny sensors. When it comes to deploying IoT, everyone agrees that security is the biggest issue. This is due to the fact that a large number of IoT devices exist in the physical world and that many of them have constrained More >

  • Open Access

    ARTICLE

    A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

    Yurong Luo, Wei You*, Chao Shang, Xiongpeng Ren, Jin Cao, Hui Li

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2237-2260, 2024, DOI:10.32604/cmes.2023.045679

    Abstract The dynamic landscape of the Internet of Things (IoT) is set to revolutionize the pace of interaction among entities, ushering in a proliferation of applications characterized by heightened quality and diversity. Among the pivotal applications within the realm of IoT, as a significant example, the Smart Grid (SG) evolves into intricate networks of energy deployment marked by data integration. This evolution concurrently entails data interchange with other IoT entities. However, there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem. In this paper, we introduce a More >

  • Open Access

    ARTICLE

    Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism

    Sanxiu Jiao1, Lecai Cai2,*, Jintao Meng3, Yue Zhao3, Kui Cheng2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 247-265, 2024, DOI:10.32604/csse.2023.040194

    Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while More >

  • Open Access

    REVIEW

    Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

    Jiansheng Peng1,2,*, Jinsong Guo1, Fengbo Bao1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897

    Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of… More >

  • Open Access

    ARTICLE

    YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

    Fares Alharbi1, Reem Alshahrani2, Mohammed Zakariah3,*, Amjad Aldweesh1, Abdulrahman Abdullah Alghamdi1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3697-3722, 2023, DOI:10.32604/cmc.2023.040086

    Abstract Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing… More >

  • Open Access

    ARTICLE

    Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

    Chunlin Xiong1,2, Zhengqiu Weng3,4,*, Jia Liu1, Liang Gu2, Fayez Alqahtani5, Amr Gafar6, Pradip Kumar Sharma7

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088

    Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly… More >

  • Open Access

    ARTICLE

    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

Displaying 21-30 on page 3 of 217. Per Page