Sultan Mesfer Aldossary^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3387-3404, 2023, DOI:10.32604/iasc.2023.035249

Abstract With the emergence of cloud technologies, the services of healthcare systems have grown. Simultaneously, machine learning systems have become important tools for developing matured and decision-making computer applications. Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services. However, in some areas, these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease (DKD) while ensuring privacy preservation of the medical data. To address the cloud data privacy problem, we proposed a DKD prediction module in a framework using cloud computing services and a data… More >

Yiqun Zhu¹, Shuxian Sun¹, Chunyu Liu¹, Xinyi Tian¹, Jingyi He², Shuai Xiao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3285-3297, 2023, DOI:10.32604/cmes.2023.026691

Abstract With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based federated learning method is proposed… More >

R. Bala Krishnan¹, D. Yuvaraj², P. Suthanthira Devi³, Varghese S. Chooralil⁴, N. Rajesh Kumar¹, B. Karthikeyan⁵, G. Manikandan^5,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1563-1576, 2023, DOI:10.32604/csse.2023.035307

Abstract With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner… More >

A. Francis Saviour Devaraj¹, T. Satyanarayana Murthy², Fayadh Alenezi³, E. Laxmi Lydia⁴, Mohamad Adzhar Md Zawawi⁵, Mohamad Khairi Ishak^5,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1431-1445, 2023, DOI:10.32604/csse.2023.034421

Abstract Recently, a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks (WSN). In routing, the trust system includes or avoids nodes related to the estimated trust values in the routing function. This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol (EMTA-SRSP) for WSN. The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN. To accomplish this, the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication. For the clustering process, the nodes with maximum residual energy will be considered… More >

Mingshuai Sheng¹, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2,3, Jing Liu⁴, Mengxing Huang^1,5, Yen-Wei Chen⁶

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 293-309, 2023, DOI:10.32604/cmc.2023.036438

Abstract Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key… More >

Rupali Gangarde^1,2,*, Amit Sharma³, Ambika Pawar⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2171-2190, 2023, DOI:10.32604/cmc.2023.035559

Abstract Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome… More >

Mao Yuxin, Wang Honglin^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879

Abstract In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without… More >

Shujuan Tian^1,2,3, Wenjian Ding^1,2,3, Gang Liu⁴, Yuxia Sun⁵, Saiqin Long⁵, Jiang Zhu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 373-391, 2023, DOI:10.32604/cmc.2023.034770

Abstract In edge computing, a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion. To maximize such benefits, this paper proposes a dynamic multi-attribute resource bidding mechanism (DMRBM). Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits. It is worth noting that when edge providers and users trade with third-party agents which are not entirely reliable and trustworthy, their sensitive information is prone to be leaked. Moreover, the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction… More >

Mingyu Dong, Diqun Yan^*, Rangding Wang

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1-12, 2023, DOI:10.32604/csse.2023.034568

Abstract Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in speech. In this work, we… More >

Ze Xu, Sanxing Cao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 861-881, 2023, DOI:10.32604/cmes.2023.025159

Abstract Multi-Source data plays an important role in the evolution of media convergence. Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data. However, it also faces serious problems in terms of protecting user and data privacy. Many privacy protection methods have been proposed to solve the problem of privacy leakage during the process of data sharing, but they suffer from two flaws: 1) the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain; 2) the inability to solve… More > Graphic Abstract