Sultan Mesfer Aldossary^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2199-2212, 2023, DOI:10.32604/iasc.2023.038243 - 21 June 2023

Abstract The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation… More >

Mohammad S. Al-kahtani¹, Zahid Mehmood^2,3,*, Tariq Sadad⁴, Islam Zada⁵, Gauhar Ali⁶, Mohammed ElAffendi⁶

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673 - 21 June 2023

Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) More >

Chen Wang¹, Jian Xu^1,*, Jiarun Li¹, Yan Dong¹, Nitin Naik²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1421-1436, 2023, DOI:10.32604/iasc.2023.034123 - 21 June 2023

Abstract Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data… More >

Faguo Wu^1,2,3,4,*, Bo Zhou², Jie Jiang⁵, Tianyu Lei¹, Jiale Song¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 957-973, 2023, DOI:10.32604/cmc.2023.038771 - 08 June 2023

Abstract With the rapid increase in demand for data trustworthiness and data security, distributed data storage technology represented by blockchain has received unprecedented attention. These technologies have been suggested for various uses because of their remarkable ability to offer decentralization, high autonomy, full process traceability, and tamper resistance. Blockchain enables the exchange of information and value in an untrusted environment. There has been a significant increase in attention to the confidentiality and privacy preservation of blockchain technology. Ensuring data privacy is a critical concern in cryptography, and one of the most important protocols used to achieve… More >

Chaoyang Li^*, Bohao Jiang, Yanbu Guo, Xiangjun Xin

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 591-606, 2023, DOI:10.32604/cmc.2023.038129 - 08 June 2023

Abstract Blockchain technology promotes the development of the Internet of medical things (IoMT) from the centralized form to distributed trust mode as blockchain-based Internet of medical things (BIoMT). Although blockchain improves the cross-institution data sharing ability, there still exist the problems of authentication difficulty and privacy leakage. This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing. This BIoMT system is divided into four layers: perceptual, network, platform, and application. The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger.… More >

Junqi Guo^1,2, Qingyun Xiong^1,*, Minghui Yang¹, Ziyun Zhao¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 827-848, 2023, DOI:10.32604/cmc.2023.036450 - 08 June 2023

Abstract Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time… More >

Zaid Abdulsalam Ibrahim^1,*, Muhammad Ilyas²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 939-956, 2023, DOI:10.32604/cmc.2023.035798 - 08 June 2023

Abstract Most research works nowadays deal with real-time Internet of Things (IoT) data. However, with exponential data volume increases, organizations need help storing such humongous amounts of IoT data in cloud storage systems. Moreover, such systems create security issues while efficiently using IoT and Cloud Computing technologies. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has the potential to make IoT data more secure and reliable in various cloud storage services. Cloud-assisted IoTs suffer from two privacy issues: access policies (public) and super polynomial decryption times (attributed mainly to complex access structures). We have developed a CP-ABE scheme in alignment… More >

Jiansheng Zhang¹, Yang Xin^1,*, Yuyan Wang², Xiaohui Lei², Yixian Yang¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6185-6199, 2023, DOI:10.32604/cmc.2023.038029 - 29 April 2023

Abstract The car-hailing platform based on Internet of Vehicles (IoV) technology greatly facilitates passengers’ daily car-hailing, enabling drivers to obtain orders more efficiently and obtain more significant benefits. However, to match the driver closest to the passenger, it is often necessary to process the location information of the passenger and driver, which poses a considerable threat to privacy disclosure to the passenger and driver. Targeting these issues, in this paper, by combining blockchain and Paillier homomorphic encryption algorithm, we design a secure blockchain-enabled IoV scheme with privacy protection for online car-hailing. In this scheme, firstly, we… More >

Mohammad Yamin^1,*, Tom Gedeon², Saleh Bajaba³, Mona M. Abusurrah⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5659-5674, 2023, DOI:10.32604/cmc.2023.034005 - 29 April 2023

Abstract In recent years, the demand for biometric-based human recognition methods has drastically increased to meet the privacy and security requirements. Palm prints, palm veins, finger veins, fingerprints, hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition techniques. Amongst the available biometric recognition techniques, Finger Vein Recognition (FVR) is a general technique that analyzes the patterns of finger veins to authenticate the individuals. Deep Learning (DL)-based techniques have gained immense attention in the recent years, since it accomplishes excellent outcomes in various challenging domains such as computer vision,… More >

You Lu^1,2,#,*, Linqian Cui^1,2,#,*, Yunzhe Wang^1,2, Jiacheng Sun^1,2, Lanhui Liu³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032 - 23 April 2023

Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may… More >