Bin Wu^1,2, Xianyi Chen³, Jinzhou Huang^4,*, Caicai Zhang⁵, Jing Wang⁶, Jing Yu^1,2, Zhiqiang Zhao⁷, Zhuolin Mei^1,2

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3177-3194, 2024, DOI:10.32604/cmc.2023.047147

Abstract In a cloud environment, outsourced graph data is widely used in companies, enterprises, medical institutions, and so on. Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers. Servers on cloud platforms usually have some subjective or objective attacks, which make the outsourced graph data in an insecure state. The issue of privacy data protection has become an important obstacle to data sharing and usage. How to query outsourcing graph data safely and effectively has become the focus of research. Adjacency query is a basic… More >

Muhammad Saad¹, Muhammad Raheel Bhutta², Jongik Kim^3,*, Tae-Sun Chung¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4263-4282, 2024, DOI:10.32604/cmc.2024.047132

Abstract With the increase in IoT (Internet of Things) devices comes an inherent challenge of security. In the world today, privacy is the prime concern of every individual. Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage. The challenge is keeping confidentiality and continuing to make the person innominate throughout the system. To address this, we present our proposed architecture where we manage IoT devices using blockchain technology. Our proposed architecture works on… More >

Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the More >

Fauziyah¹, Zhaoshun Wang^1,*, Mujahid Tabassum²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4417-4452, 2024, DOI:10.32604/cmc.2024.046797

Abstract In an era characterized by digital pervasiveness and rapidly expanding datasets, ensuring the integrity and reliability of information is paramount. As cyber threats evolve in complexity, traditional cryptographic methods face increasingly sophisticated challenges. This article initiates an exploration into these challenges, focusing on key exchanges (encompassing their variety and subtleties), scalability, and the time metrics associated with various cryptographic processes. We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering. Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity, foundational pillars of information security. Our method… More >

Xiaoyan Zhu¹, Ruchun Jia², Tingrui Zhang³, Song Yao^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4363-4377, 2024, DOI:10.32604/cmc.2023.045615

Abstract The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect, which is easy to leads to the problem that the data is usurped. Starting from the application of the ATC (automatic train control) network, this paper focuses on the zero trust and zero trust access strategy and the tamper-proof method of information-sharing network data. Through the improvement of ATC’s zero trust physical layer authentication and network data distributed feature differentiation calculation, this paper reconstructs the personal privacy scope authentication structure and designs a tamper-proof method of… 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 More >

Jinbo Yang¹, Hai Huang¹, Lailai Yin², Jiaxing Qu³, Wanjuan Xie^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the… More >

Dan Zhao¹, Yang You², Chuanwen Luo^3,*, Ting Chen^4,*, Yang Liu⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3063-3083, 2024, DOI:10.32604/cmes.2023.045400

Abstract In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not… More >

Yanzhong Sun¹, Xiaoni Du^1,*, 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 >

Yuming Tang^1,#, Yitian Zhang^2,#, Tao Niu¹, Zhen Li^2,3,*, Zijian Zhang^1,3, 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 >