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  • Open Access

    ARTICLE

    Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

    Tao Yin1, Changgen Peng2,*, Weijie Tan3, Dequan Xu4, Hanlin Tang5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 827-843, 2024, DOI:10.32604/cmes.2023.029039

    Abstract In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection… More >

  • Open Access

    ARTICLE

    Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption

    Chen Wang1, Jian Xu1,*, Jiarun Li1, Yan Dong1, Nitin Naik2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1421-1436, 2023, DOI:10.32604/iasc.2023.034123

    Abstract Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data with different public keys. At… More >

  • Open Access

    ARTICLE

    An Efficient Encrypted Speech Retrieval Based on Unsupervised Hashing and B+ Tree Dynamic Index

    Qiu-yu Zhang*, Yu-gui Jia, Fang-Peng Li, Le-Tian Fan

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 107-128, 2023, DOI:10.32604/cmc.2023.038911

    Abstract Existing speech retrieval systems are frequently confronted with expanding volumes of speech data. The dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’ real-time retrieval requirements. This study proposes an efficient method for retrieving encryption speech, using unsupervised deep hashing and B+ tree dynamic index, which avoid privacy leakage of speech data and enhance the accuracy and efficiency of retrieval. The cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan (DGHV) Fully Homomorphic Encryption (FHE) technique, which encrypts the original speech. In addition, this research employs… More >

  • Open Access

    ARTICLE

    An Effective Security Comparison Protocol in Cloud Computing

    Yuling Chen1,2, Junhong Tao1, Tao Li1,*, Jiangyuan Cai3, Xiaojun Ren4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5141-5158, 2023, DOI:10.32604/cmc.2023.037783

    Abstract With the development of cloud computing technology, more and more data owners upload their local data to the public cloud server for storage and calculation. While this can save customers’ operating costs, it also poses privacy and security challenges. Such challenges can be solved using secure multi-party computation (SMPC), but this still exposes more security issues. In cloud computing using SMPC, clients need to process their data and submit the processed data to the cloud server, which then performs the calculation and returns the results to each client. Each client and server must be honest. If there is cooperation or… More >

  • Open Access

    ARTICLE

    Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

    Zhen Wang, Qiu-yu Zhang*, Ling-tao Meng, Yi-lin Liu

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6161-6184, 2023, DOI:10.32604/cmc.2023.037134

    Abstract To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and… More >

  • Open Access

    REVIEW

    A Survey of Privacy Preservation for Deep Learning Applications

    Ling Zhang1,*, Lina Nie1, Leyan Yu2

    Journal of Information Hiding and Privacy Protection, Vol.4, No.2, pp. 69-78, 2022, DOI:10.32604/jihpp.2022.039284

    Abstract Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly… More >

  • Open Access

    ARTICLE

    Asymmetric Consortium Blockchain and Homomorphically Polynomial-Based PIR for Secured Smart Parking Systems

    T. Haritha, A. Anitha*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3923-3939, 2023, DOI:10.32604/cmc.2023.036278

    Abstract In crowded cities, searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’ time, increases air pollution, and traffic congestion. Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement. But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number, personal identity, and desired destination. This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’ security breaches, single points of failure,… 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

    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

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

  • Open Access

    ARTICLE

    Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

    S. Vidhya1,*, V. Kalaivani2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2673-2692, 2023, DOI:10.32604/csse.2023.027724

    Abstract In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored… More >

  • Open Access

    ARTICLE

    Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

    Mesfer Al Duhayyim1,*, Mohammed Maray2, Ayman Qahmash2, Fatma S. Alrayes3, Nuha Alshuqayran4, Jaber S. Alzahrani5, Mohammed Alghamdi2,6, Abdullah Mohamed7

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3133-3149, 2023, DOI:10.32604/cmc.2023.032740

    Abstract Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services’ domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is required in order to raise… More >

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