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  • Open Access

    ARTICLE

    A Fully Homomorphic Encryption Scheme Suitable for Ciphertext Retrieval

    Ronglei Hu1, Chuce He1,2, Sihui Liu1, Dong Yao1, Xiuying Li1, Xiaoyi Duan1,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 937-956, 2025, DOI:10.32604/cmc.2025.062542 - 09 June 2025

    Abstract Ciphertext data retrieval in cloud databases suffers from some critical limitations, such as inadequate security measures, disorganized key management practices, and insufficient retrieval access control capabilities. To address these problems, this paper proposes an enhanced Fully Homomorphic Encryption (FHE) algorithm based on an improved DGHV algorithm, coupled with an optimized ciphertext retrieval scheme. Our specific contributions are outlined as follows: First, we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data. Second, a triple-key encryption mechanism, which separates the data encryption key, retrieval authorization key, More >

  • Open Access

    ARTICLE

    A Blockchain Cross-Chain Transaction Protection Scheme Based on FHE

    Hongliang Tian, Zuoqing Li*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3983-4002, 2025, DOI:10.32604/cmc.2025.059103 - 06 March 2025

    Abstract Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes. To address these issues, a blockchain cross-chain transaction protection scheme based on Fully Homomorphic Encryption (FHE) is proposed. In the proposed scheme, the functional relationship is established by Box-Muller, Discrete Gaussian Distribution Function (DGDF) and Uniform Random Distribution Function (URDF) are used to improve the security and efficiency of key generation. Subsequently, the data preprocessing function is introduced to perform cleaning, deduplication, and normalization operations on the transaction data of multi-key signature, and it is classified into… More >

  • Open Access

    ARTICLE

    An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy Masking

    Cong Shen1,*, Wei Zhang1,2,*, Tanping Zhou1,2, Yiming Zhang1, Lingling Zhang3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

    Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

  • Open Access

    ARTICLE

    FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption

    Hao Sun*, Xiubo Chen, Kaiguo Yuan

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159 - 15 May 2024

    Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417 - 11 March 2024

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the… More >

  • Open Access

    ARTICLE

    Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

    Mohanad A. Mohammed*, Hala B. Abdul Wahab

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1731-1748, 2024, DOI:10.32604/cmes.2023.030528 - 17 November 2023

    Abstract Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges. Concurrently, the Internet of Things (IoT) has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services, ultimately enhancing human lives. This paper presents a new approach utilizing lightweight blockchain technology, effectively reducing the computational burden typically associated with conventional blockchain systems. By integrating this lightweight blockchain with IoT systems, substantial reductions in implementation time and computational complexity can be achieved. Moreover, the paper proposes the utilization of the Okamoto More >

  • 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 - 22 September 2023

    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… 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 - 21 June 2023

    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… 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 - 08 June 2023

    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.… 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 - 29 April 2023

    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.… More >

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