Youlin Ji, Bo Yin^*, Ke Gu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4205-4223, 2024, DOI:10.32604/cmc.2024.049086

Abstract With its untameable and traceable properties, blockchain technology has been widely used in the field of data sharing. How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing. In this paper, we study verifiable keyword frequency (KF) queries with local differential privacy in blockchain. Both the numerical and the keyword attributes are present in data objects; the latter are sensitive and require privacy protection. However, prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries. We propose an efficient framework that… More >

Nannan Wu¹, Xianyi Chen^1,*, James Msughter Adeke², Junjie Zhao²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3001-3019, 2024, DOI:10.32604/cmc.2023.045311

Abstract Recently, deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding. However, these approaches have some limitations. For example, a cover image lacks self-adaptability, information leakage, or weak concealment. To address these issues, this study proposes a universal and adaptable image-hiding method. First, a domain attention mechanism is designed by combining the Atrous convolution, which makes better use of the relationship between the secret image domain and the cover image domain. Second, to improve perceived human similarity, perceptual loss is incorporated into the training process. The experimental results are promising, with the proposed method achieving an… More >

Jiansheng Peng^1,2,*, 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 federated learning and multi-robot systems… More >

Yonghao Tang¹, Zhiping Cai^1,*, 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 to regain security when finding… More >

Quan Zhou^1,*, 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 signature using the Diffie-Hellman key… More >

Shoujian Yu¹, Zhenchi Jie¹, Guowen Wu¹, Hong Zhang¹, Shigen Shen^2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1729-1751, 2023, DOI:10.32604/iasc.2023.039911

Abstract In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While… 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 >

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 >

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 >

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 >