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  • Open Access

    ARTICLE

    A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

    Shiqi Wang1, Mingfang Jiang2,*, Jiaohua Qin1, Hengfu Yang2, Zhichen Gao3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2979-2993, 2021, DOI:10.32604/cmc.2021.017094

    Abstract In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called More >

  • Open Access

    ARTICLE

    Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

    Sajid Habib Gill1, Noor Ahmed Sheikh1, Samina Rajpar1, Zain ul Abidin2, N. Z. Jhanjhi3,*, Muneer Ahmad4, Mirza Abdur Razzaq1, Sultan S. Alshamrani5, Yasir Malik6, Fehmi Jaafar7

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3773-3787, 2021, DOI:10.32604/cmc.2021.016001

    Abstract Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural… More >

  • Open Access

    ARTICLE

    XGBoost Algorithm under Differential Privacy Protection

    Yuanmin Shi1,2, Siran Yin1,2, Ze Chen1,2, Leiming Yan1,2,*

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193

    Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the More >

  • Open Access

    ARTICLE

    HealthyBlockchain for Global Patients

    Shada A. Alsalamah1,2,3,*, Hessah A. Alsalamah1,4, Thamer Nouh5, Sara A. Alsalamah6

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2431-2449, 2021, DOI:10.32604/cmc.2021.016618

    Abstract An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making processes. Current healthcare information systems (HISs) fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care models. Meanwhile, in recent times, the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to patients. No blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in healthcare. In this paper, More >

  • Open Access

    ARTICLE

    Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

    Mueen Uddin1,*, M. S. Memon2, Irfana Memon2, Imtiaz Ali2, Jamshed Memon3, Maha Abdelhaq4, Raed Alsaqour5

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2377-2397, 2021, DOI:10.32604/cmc.2021.015354

    Abstract Background: Electronic Health Record (EHR) systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally. However, the existing EHR systems mostly lack in providing appropriate security, entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures. Objective: To solve this delicate problem, we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems. Methodology: In our EHR blockchain system, Peer nodes from various organizations (stakeholders)… More >

  • Open Access

    ARTICLE

    Smart Contract: Security and Privacy

    Leena S. Alotaibi, Sultan S. Alshamrani*

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 93-101, 2021, DOI:10.32604/csse.2021.015547

    Abstract Smart contracts are simply self-activated contracts between two parties. The idea behind their implementation relies on the concept of blockchain, wherein the details and execution of the contract are turned into code and distributed among users of a network. This process controls counterfeiting and money laundering by its ability to trace who owes whom. It also boosts the general economy. This research paper shows how smart contracts in modern-day systems have changed the approach to money tracing. We present case studies about the uses of smart contracts with high levels of security and privacy. As More >

  • Open Access

    ARTICLE

    Evaluating the Risk of Disclosure and Utility in a Synthetic Dataset

    Kang-Cheng Chen1, Chia-Mu Yu2,*, Tooska Dargahi3

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 761-787, 2021, DOI:10.32604/cmc.2021.014984

    Abstract The advancement of information technology has improved the delivery of financial services by the introduction of Financial Technology (FinTech). To enhance their customer satisfaction, Fintech companies leverage artificial intelligence (AI) to collect fine-grained data about individuals, which enables them to provide more intelligent and customized services. However, although visions thereof promise to make customers’ lives easier, they also raise major security and privacy concerns for their users. Differential privacy (DP) is a common privacy-preserving data publishing technique that is proved to ensure a high level of privacy preservation. However, an important concern arises from the… More >

  • Open Access

    ARTICLE

    Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

    Alaa Omran Almagrabi1, Rashid Ali2, Daniyal Alghazzawi1, Abdullah AlBarakati1, Tahir Khurshaid3,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 359-376, 2021, DOI:10.32604/cmc.2021.014753

    Abstract The scope of the Internet of Things (IoT) applications varies from strategic applications, such as smart grids, smart transportation, smart security, and smart healthcare, to industrial applications such as smart manufacturing, smart logistics, smart banking, and smart insurance. In the advancement of the IoT, connected devices become smart and intelligent with the help of sensors and actuators. However, issues and challenges need to be addressed regarding the data reliability and protection for significant next-generation IoT applications like smart healthcare. For these next-generation applications, there is a requirement for far-reaching privacy and security in the IoT.… More >

  • Open Access

    ARTICLE

    Cyber Security and Privacy Issues in Industrial Internet of Things

    NZ Jhanjhi1, Mamoona Humayun2,*, Saleh N. Almuayqil2

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 361-380, 2021, DOI:10.32604/csse.2021.015206

    Abstract The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades. Consequently, there has been a huge paradigm shift in the manufacturing and production sectors. However, this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting (various pillars of) industry 4.0. However, before providing a concrete solution certain aspect need to be researched, for instance, cybersecurity threats and privacy issues in the industry. To fill this gap, this paper discusses potential solutions to cybersecurity targeting this industry and highlights… More >

  • Open Access

    ARTICLE

    OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment

    Jian Tang, Zhihua Xia*, Lan Wang, Chengsheng Yuan, Xueli Zhao

    Journal on Big Data, Vol.3, No.1, pp. 21-33, 2021, DOI:10.32604/jbd.2021.015892

    Abstract As the wide application of imaging technology, the number of big image data which may containing private information is growing fast. Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management. But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage. However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme. This paper proposes an outsourcing… More >

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