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  • Open Access

    ARTICLE

    Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

    A. Francis Saviour Devaraj1, T. Satyanarayana Murthy2, Fayadh Alenezi3, E. Laxmi Lydia4, Mohamad Adzhar Md Zawawi5, Mohamad Khairi Ishak5,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1431-1445, 2023, DOI:10.32604/csse.2023.034421 - 09 February 2023

    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 More >

  • Open Access

    ARTICLE

    Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

    Mingshuai Sheng1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2,3, Jing Liu4, Mengxing Huang1,5, Yen-Wei Chen6

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 293-309, 2023, DOI:10.32604/cmc.2023.036438 - 06 February 2023

    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… More >

  • Open Access

    ARTICLE

    Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

    Rupali Gangarde1,2,*, Amit Sharma3, Ambika Pawar4

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2171-2190, 2023, DOI:10.32604/cmc.2023.035559 - 06 February 2023

    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… More >

  • Open Access

    ARTICLE

    Federated Learning Based on Data Divergence and Differential Privacy in Financial Risk Control Research

    Mao Yuxin, Wang Honglin*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879 - 06 February 2023

    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… More >

  • Open Access

    ARTICLE

    A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

    Shujuan Tian1,2,3, Wenjian Ding1,2,3, Gang Liu4, Yuxia Sun5, Saiqin Long5, Jiang Zhu1,2,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 373-391, 2023, DOI:10.32604/cmc.2023.034770 - 06 February 2023

    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… More >

  • Open Access

    ARTICLE

    Adversarial Examples Protect Your Privacy on Speech Enhancement System

    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 - 20 January 2023

    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… More >

  • Open Access

    ARTICLE

    Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

    Ze Xu, Sanxing Cao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 861-881, 2023, DOI:10.32604/cmes.2023.025159 - 05 January 2023

    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;… More > Graphic Abstract

    Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

  • Open Access

    ARTICLE

    Trust and QoS-Driven Query Service Provisioning Using Optimization

    K. Narmatha1,*, K. Karthikeyan2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1827-1844, 2023, DOI:10.32604/iasc.2023.028473 - 05 January 2023

    Abstract The growing advancements with the Internet of Things (IoT) devices handle an enormous amount of data collected from various applications like healthcare, vehicle-based communication, and smart city. This research analyses cloud-based privacy preservation over the smart city based on query computation. However, there is a lack of resources to handle the incoming data and maintain them with higher privacy and security. Therefore, a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment. A querying service model is proposed to handle the incoming data collected from various… More >

  • Open Access

    ARTICLE

    Research on Federated Learning Data Sharing Scheme Based on Differential Privacy

    Lihong Guo*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5069-5085, 2023, DOI:10.32604/cmc.2023.034571 - 28 December 2022

    Abstract To realize data sharing, and to fully use the data value, breaking the data island between institutions to realize data collaboration has become a new sharing mode. This paper proposed a distributed data security sharing scheme based on C/S communication mode, and constructed a federated learning architecture that uses differential privacy technology to protect training parameters. Clients do not need to share local data, and they only need to upload the trained model parameters to achieve data sharing. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible… More >

  • Open Access

    ARTICLE

    A GDPR Compliant Approach to Assign Risk Levels to Privacy Policies

    Abdullah R. Alshamsan1, Shafique A. Chaudhry1,2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4631-4647, 2023, DOI:10.32604/cmc.2023.034039 - 28 December 2022

    Abstract Data privacy laws require service providers to inform their customers on how user data is gathered, used, protected, and shared. The General Data Protection Regulation (GDPR) is a legal framework that provides guidelines for collecting and processing personal information from individuals. Service providers use privacy policies to outline the ways an organization captures, retains, analyzes, and shares customers’ data with other parties. These policies are complex and written using legal jargon; therefore, users rarely read them before accepting them. There exist a number of approaches to automating the task of summarizing privacy policies and assigning… More >

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