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

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 with different public keys. At… 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

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 this is the secret-sharing method.… 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

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. Utilizing the untampered blockchain 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

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 delay and data heterogeneity problems.… 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

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 with a Hidden Hierarchy Ciphertext-Policy… 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

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 propose an encryption scheme based… 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

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, speech detection and Natural Language… 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

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 have an adverse effect on… More >

Lemei Da¹, Hai Liang^1,*, Yong Ding^1,2, Yujue Wang¹, Changsong Yang¹, Huiyong Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 879-902, 2023, DOI:10.32604/cmes.2023.026309

Abstract The unmanned aerial vehicle (UAV) self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale, which can quickly and accurately complete complex tasks such as path planning, situational awareness, and information transmission. Due to the openness of the network, the UAV cluster is more vulnerable to passive eavesdropping, active interference, and other attacks, which makes the system face serious security threats. This paper proposes a Blockchain-Based Data Acquisition (BDA) scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario. Each UAV cluster has an… More >

Ling Zhang^1,*, Lina Nie¹, Leyan Yu²

Journal of Information Hiding and Privacy Protection, Vol.4, No.2, pp. 69-78, 2022, DOI:10.32604/jihpp.2022.039284

Abstract Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly… More >