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  • Open Access

    ARTICLE

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

    Sandeep Dasari, Rajesh Kaluri*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2035-2051, 2024, DOI:10.32604/cmes.2024.049152 - 20 May 2024

    Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the… More > Graphic Abstract

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

  • Open Access

    ARTICLE

    A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

    Yurong Luo, Wei You*, Chao Shang, Xiongpeng Ren, Jin Cao, Hui Li

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2237-2260, 2024, DOI:10.32604/cmes.2023.045679 - 29 January 2024

    Abstract The dynamic landscape of the Internet of Things (IoT) is set to revolutionize the pace of interaction among entities, ushering in a proliferation of applications characterized by heightened quality and diversity. Among the pivotal applications within the realm of IoT, as a significant example, the Smart Grid (SG) evolves into intricate networks of energy deployment marked by data integration. This evolution concurrently entails data interchange with other IoT entities. However, there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem. In this paper, we introduce a More >

  • Open Access

    ARTICLE

    Chest Radiographs Based Pneumothorax Detection Using Federated Learning

    Ahmad Almadhor1,*, Arfat Ahmad Khan2, Chitapong Wechtaisong3,*, Iqra Yousaf4, Natalia Kryvinska5, Usman Tariq6, Haithem Ben Chikha1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.039007 - 28 July 2023

    Abstract Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data.… More >

  • Open Access

    ARTICLE

    DeepGan-Privacy Preserving of HealthCare System Using DL

    Sultan Mesfer Aldossary*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2199-2212, 2023, DOI:10.32604/iasc.2023.038243 - 21 June 2023

    Abstract The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation… More >

  • Open Access

    ARTICLE

    An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

    Hasanien K. Kuba1, Mustafa A. Azzawi2, Saad M. Darwish3,*, Oday A. Hassen4, Ansam A. Abdulhussein5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4119-4133, 2023, DOI:10.32604/cmc.2023.033182 - 31 October 2022

    Abstract It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association… More >

  • Open Access

    ARTICLE

    Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

    C. Santhosh Kumar1, K. Vishnu Kumar2,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2893-2907, 2023, DOI:10.32604/iasc.2023.029669 - 17 August 2022

    Abstract Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy More >

  • Open Access

    ARTICLE

    Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

    Neetika Bhandari1,*, Payal Pahwa2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2043-2055, 2023, DOI:10.32604/iasc.2023.028368 - 19 July 2022

    Abstract Data is humongous today because of the extensive use of World Wide Web, Social Media and Intelligent Systems. This data can be very important and useful if it is harnessed carefully and correctly. Useful information can be extracted from this massive data using the Data Mining process. The information extracted can be used to make vital decisions in various industries. Clustering is a very popular Data Mining method which divides the data points into different groups such that all similar data points form a part of the same group. Clustering methods are of various types. More >

  • Open Access

    ARTICLE

    Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

    B. Murugeshwari1,*, D. Selvaraj2, K. Sudharson3, S. Radhika4

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 839-851, 2023, DOI:10.32604/iasc.2023.028548 - 06 June 2022

    Abstract Protecting the privacy of data in the multi-cloud is a crucial task. Data mining is a technique that protects the privacy of individual data while mining those data. The most significant task entails obtaining data from numerous remote databases. Mining algorithms can obtain sensitive information once the data is in the data warehouse. Many traditional algorithms/techniques promise to provide safe data transfer, storing, and retrieving over the cloud platform. These strategies are primarily concerned with protecting the privacy of user data. This study aims to present data mining with privacy protection (DMPP) using precise elliptic More >

  • Open Access

    ARTICLE

    Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

    K. Pradeep Mohan Kumar1, Jenifer Mahilraj2, D. Swathi3, R. Rajavarman4, Subhi R. M. Zeebaree5, Rizgar R. Zebari6, Zryan Najat Rashid7, Ahmed Alkhayyat8,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5299-5314, 2022, DOI:10.32604/cmc.2022.030825 - 28 July 2022

    Abstract Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal… More >

  • Open Access

    ARTICLE

    Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

    Mohammad Alamgeer1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Nazik Alturki3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Ishfaq Yaseen4, Mohamed I. Eldesouki5

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.028275 - 18 May 2022

    Abstract Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique… More >

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