The development of fifth-generation (5G) technology marks a significant milestone for digital communication systems, providing substantial improvements in data transmission speeds and enabling enhanced connectivity across a wider range of devices. However, this rapid increase in data volume also introduces new challenges related to transmission latency, reliability, and security. This paper introduces KyMLP-LDPC, a novel approach that integrates a multi-layer parallel LDPC (MLP-LDPC) algorithm with Kyber, a post-quantum cryptography scheme, to accelerate and enable reliable and secure transmission. MLP-LDPC partitions the LDPC parity check matrix into processing groups to streamline parallel decoding and minimize message collisions during transmission, thereby accelerating error correction operations. Kyber encrypts data preemptively to safeguard against potential attacks. The effectiveness of our proposed method is evaluated using both image data and signals transmitted through an additive white Gaussian noise communication channel. Evaluation results demonstrate that the proposed method achieves superior performance in terms of error correction capabilities and data security compared to existing approaches.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a highperformance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.

Homomorphic encryption (HE) schemes, such as the Cheon-Kim-Kim-Song (CKKS) algorithm, offer immense potential for secure computation on encrypted data, particularly within privacy-sensitive domains like machine learning. However, the computational overhead inherent in CKKS operations often poses challenges, particularly for processing large-scale datasets and complex computations. In this paper, we present a new approach to mitigate these challenges by integrating innovative data compression techniques, resulting in a staggering 90% reduction in data size. Coupled with harnessing the computational prowess of graphics processing units (GPUs), our experimental results showcase remarkable achievements, with processing times reduced by up to 100 times compared to traditional methods. Our method ensures data confidentiality while addressing performance bottlenecks in CKKS-based computations, paving the way for efficient and scalable HE applications.

This paper addresses the challenges of data privacy and computational efficiency in artificial intelligence (AI) models by proposing a novel hybrid model that combines homomorphic encryption (HE) with AI to enhance security while maintaining learning accuracy. The novelty of our model lies in the introduction of a new matrix transformation technique that ensures compatibility with both HE algorithms and AI model weight matrices, significantly improving computational efficiency. Furthermore, we present a first-of-its-kind mathematical proof of convergence for integrating HE into AI models using the adaptive moment estimation optimization algorithm. The effectiveness and practicality of our approach for training on encrypted data are showcased through comprehensive evaluations of well-known datasets for air pollution forecasting and forest fire detection. These successful results demonstrate high model performance, with nearly 1 R-squared for air pollution forecasting and 99% accuracy for forest fire detection. Additionally, our approach achieves a reduction of up to 90% in data storage and a tenfold increase in speed compared to models that do not use the matrix transformation method. Our primary contribution lies in enhancing the security, efficiency, and dependability of AI models, particularly when dealing with sensitive data.  

Global healthcare systems face growing pressure as populations rise. This can lead to longer wait times and an increased risk of treatment delays or misdiagnosis. Artificial intelligence (AI) diagnostic systems are being developed to address these challenges, but concerns exist about their accuracy and data security. This study introduces a robust AI telehealth system that offers a two-pronged approach. It utilizes a cutting-edge image analysis method, vision transformer, to enhance diagnostic accuracy, while also incorporating post-quantum cryptography algorithm, Kyber, to ensure patient privacy. Furthermore, an interactive visualization tool aids in interpreting the diagnostic results, providing valuable insights into the model's decisionmaking process. This translates to faster diagnoses and potentially shorter wait times for patients. Extensive testing with various datasets has demonstrated the system's effectiveness. The optimized model achieves a remarkable 95.79% accuracy rate in diagnosing COVID-19 from chest X-rays, with the entire process completed in under five seconds.

Supercomputers poised to crack current encryption standards within a decade, traditional methods face an unprecedented threat. Training artificial intelligence (AI) models on plaintext data has sparked increasing concerns regarding privacy and security. The potential risks include the possibility of data leakage or theft. To address these critical challenges, we propose a groundbreaking architecture that seamlessly integrates homomorphic encryption (HE) and AI, enabling privacypreserving analysis of sensitive audio data and paving the way for a new era of secure AI applications in audio processing. Our approach introduces novel approximations for the sigmoid (Asigmoid) and rectified linear unit (A-ReLU) activation functions, designed to be compatible with the input data distribution and to minimize the percentage of squared error (PSE) between the approximation and the original activation function, optimizing processing efficiency on encrypted data while overcoming the limitations of existing methods. We underscore the crucial role of activation function selection and HE's parameter tuning in achieving a balance between computational efficiency and model accuracy within this framework. The evaluation results using the audioMNIST and musical instrument datasets demonstrate the system's robustness with a negligible 0.04% difference between plaintext and ciphertext conditions, highlighting its promising potential for secure audio data processing in various applications.

The fifth-generation new radio coding techniques, such as low-density parity-check (LDPC) codes, have made significant progress. However, their limitations in the low error floor region prevent them from meeting the stringent bit error rate (BER) targets required for sixth-generation (6G) communications. 6G aims to support services with BER as low as 10 −9 , providing the reliability needed for mission-critical applications. his is particularly crucial for ultra-reliable lowlatency communication. In this paper, we present a two-stage decoding method that combines the strengths of layered LDPC decoding and federated transfer learning (FTL). The layered LDPC decoding stage effectively handles the challenging waterfall region, reducing errors during the initial decoding process. In addition, the FTL model adapts to mitigate the error floor and accelerate its decline. This combination achieves lower error rates and faster performance improvements, reducing the need for retransmissions and enhancing overall system efficiency.

Our approach leverages the massive parallelism and computational power of a modern graphics processing unit (GPU) to accelerate CRYSTALS-Dilithium's key operations. While post-quantum cryptographic algorithms like Dilithium offer robust security, they face computational challenges in high-throughput scenarios. Dilithium's performance bottlenecks, particularly in polynomial arithmetic and random sampling, have limited its practical deployment. To address these challenges, we leverage GPU acceleration as a promising solution to enhance efficiency. Our approach develops novel GPU-centric algorithms and data structures tailored to Dilithium's requirements. We focus on optimizing number theoretic transforms (NTT), polynomial arithmetic, and random sampling, with an emphasis on batch processing and efficient memory management to maximize throughput. Experimental results demonstrate significant performance improvements over existing implementations. Our GPUaccelerated version consistently outperforms reference implementations, achieving speed-ups ranging from 53.7% to 59.9% for signature generation and 20.0% to 63.8% for verification across various security levels. These results highlight the potential of our approach in enabling efficient post-quantum cryptography for high-performance applications.

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10−6 to 10−9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized lowdensity parity check (GLDPC) codes offer a more advantageous trade-off between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new securitybased GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reliability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

Federated learning (FL) enables multiple parties to collaboratively train machine learning models while preserving data privacy. However, securing communication within FL frameworks remains a significant challenge due to potential vulnerabilities to data breaches and integrity attacks. This paper proposes a novel approach using Dilithium, a robust digital signature framework, to enhance data security in FL. By integrating Dilithium into FL protocols, this study demonstrates enhanced protection against data tampering and unauthorized access, thereby promoting safer and more efficient collaborative model training across distributed networks. Furthermore, our approach incorporates an optimized key distribution mechanism that reduces latency and ensures seamless synchronization among participants. Additionally, the proposed client selection algorithm aims to minimize the time and computational load on the system. Experimental results demonstrate that our system achieves a total processing time of 6.891 seconds, significantly outperforming the 10.24 seconds of normal FL and 12.32 seconds of FL-Dilithium systems on the same computing platforms. Additionally, the proposed model achieves an accuracy of 94%, surpassing the 93% of the normal FL and 92% of the FL-Dilithium models.

This research investigates the application of machine learning for diagnosing COVID-19 from chest X-rays. We analyze various popular architectures, including efficient neural networks (EfficientNet), multiscale vision transformers (MViT), efficient vision transformers (EfficientViT), and vision transformers (ViT), on a dataset categorized into COVID, lung opacity, normal, and viral pneumonia. While multiscale models demonstrate a tendency to overfit, our proposed fine-tuning ViT model achieves significant accuracy, reaching 95.79% in four-class classification, 99.57% in a clinically relevant three-class grouping, and similarly high performance in binary classification. Validation through quantitative metrics and visualization solidifies the model's effectiveness. Comparative analysis showcases the superiority of our approach. Overall, these findings showcase the potential of ViT for accurate COVID-19 diagnosis, contributing to the advancement of medical image analysis.

In this paper, we present an integrated framework that combines Federated Learning (FL) with Homomorphic Encryption (HE) using the Artificial Intelligence (AI) models and the Cheon-Kim-Kim-Song (CKKS) algorithm to address the challenges of privacy and accuracy. FL facilitates collaborative training of Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models across decentralized devices, allowing for data privacy preservation without sharing raw data. The integration of the CKKS algorithm for HE ensures secure computation on encrypted data during the FL process. Our experimental results on three diverse datasets demonstrate the efficacy of this approach, achieving an impressive highest average accuracy of 97.3%. Additionally, the CKKS algorithm is used to achieve efficient computation, making it a promising solution for privacy-conscious machine learning applications, and paving the way for practical deployment in various real-world scenarios, thereby revolutionizing the landscape of privacy-preserving machine learning.