Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (10)
  • 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

    Side-Channel Leakage Analysis of Inner Product Masking

    Yuyuan Li1,2, Lang Li1,2,*, Yu Ou1,2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1245-1262, 2024, DOI:10.32604/cmc.2024.049882 - 25 April 2024

    Abstract The Inner Product Masking (IPM) scheme has been shown to provide higher theoretical security guarantees than the Boolean Masking (BM). This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security. Some previous work unfolds when certain (adversarial and implementation) conditions are met, and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour. In this paper, we investigate the security characteristics of IPM under different conditions. In adversarial condition, the security properties of first-order IPMs obtained through parametric characterization More >

  • Open Access

    ARTICLE

    Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

    Zihao Song, Yan Zhou*, Wei Cheng, Futai Liang, Chenhao Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034 - 26 March 2024

    Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.… More >

  • Open Access

    ARTICLE

    Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

    Tianyu He1, Xiaolong Xu1,*, Zhichen Hu1, Qingzhan Zhao2, Jianguo Dai2, Fei Dai3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3657-3673, 2023, DOI:10.32604/iasc.2023.036831 - 15 March 2023

    Abstract With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of… More >

  • Open Access

    ARTICLE

    An Ophthalmic Evaluation of Central Serous Chorioretinopathy

    L. K. Shoba1,*, P. Mohan Kumar2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 613-628, 2023, DOI:10.32604/csse.2023.024449 - 01 June 2022

    Abstract Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to More >

  • Open Access

    ARTICLE

    Image Masking and Enhancement System for Melanoma Early Stage Detection

    Fikret Yalcinkaya*, Ali Erbas

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1961-1977, 2022, DOI:10.32604/iasc.2022.024961 - 24 March 2022

    Abstract Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels’ characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has… More >

  • Open Access

    ARTICLE

    Invariant of Enhanced AES Algorithm Implementations Against Power Analysis Attacks

    Nadia Mustaqim Ansari1,*, Rashid Hussain2, Sheeraz Arif3, Syed Sajjad Hussain4

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1861-1875, 2022, DOI:10.32604/cmc.2022.023516 - 24 February 2022

    Abstract The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of IoT networks. Side Channel Attacks (SCA) are one of the major concerns. The prime objective of SCA is to acquire the information by observing the power consumption, electromagnetic (EM) field, timing analysis, and acoustics of the device. Later, the attackers perform statistical functions to recover the key. Advanced Encryption Standard (AES) algorithm has proved to be a good security solution for constrained IoT devices. This paper implements a simulation model which is used to modify the AES algorithm… More >

  • Open Access

    ARTICLE

    Parameter Estimation Based on Censored Data under Partially Accelerated Life Testing for Hybrid Systems due to Unknown Failure Causes

    Mustafa Kamal*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1239-1269, 2022, DOI:10.32604/cmes.2022.017532 - 30 December 2021

    Abstract In general, simple subsystems like series or parallel are integrated to produce a complex hybrid system. The reliability of a system is determined by the reliability of its constituent components. It is often extremely difficult or impossible to get specific information about the component that caused the system to fail. Unknown failure causes are instances in which the actual cause of system failure is unknown. On the other side, thanks to current advanced technology based on computers, automation, and simulation, products have become incredibly dependable and trustworthy, and as a result, obtaining failure data for… More >

  • Open Access

    ARTICLE

    Speech Intelligibility Enhancement Algorithm Based on Multi-Resolution Power-Normalized Cepstral Coefficients (MRPNCC) for Digital Hearing Aids

    Xia Wang1, Xing Deng2,3, Hongming Shen1,*, Guodong Zhang1, Shibing Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 693-710, 2021, DOI:10.32604/cmes.2021.013186 - 21 January 2021

    Abstract Speech intelligibility enhancement in noisy environments is still one of the major challenges for hearing impaired in everyday life. Recently, Machine-learning based approaches to speech enhancement have shown great promise for improving speech intelligibility. Two key issues of these approaches are acoustic features extracted from noisy signals and classifiers used for supervised learning. In this paper, features are focused. Multi-resolution power-normalized cepstral coefficients (MRPNCC) are proposed as a new feature to enhance the speech intelligibility for hearing impaired. The new feature is constructed by combining four cepstrum at different time–frequency (T–F) resolutions in order to… More >

  • Open Access

    ARTICLE

    The Development and Application of Quantum Masking

    Tao Chen1,2, Zhiguo Qu1,2,*, Yi Chen1,2

    Journal of Quantum Computing, Vol.2, No.3, pp. 151-156, 2020, DOI:10.32604/jqc.2020.015855 - 31 December 2020

    Abstract To solve the problem of hiding quantum information in simplified subsystems, Modi et al. [1] introduced the concept of quantum masking. Quantum masking is the encoding of quantum information by composite quantum states in such a way that the quantum information is hidden to the subsystem and spreads to the correlation of the composite systems. The concept of quantum masking was developed along with a new quantum impossibility theorem, the quantum no-masking theorem. The question of whether a quantum state can be masked has been studied by many people from the perspective of the types More >

Displaying 1-10 on page 1 of 10. Per Page