ATD Learning: A secure, smart, and decentralised learning method for big data environments

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102953 - 102953

Published: Jan. 1, 2025

Language: Английский

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An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 2, 2025

This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based employs two publicly available scan datasets which are considered as labeled and an unlabeled dataset. dataset is split into three subsets assumed be collected hospitals. Training done using the Bootstrap Your Own Latent (BYOL) contrastive SSL VGG19 encoder followed by attention CNN blocks (VGG19 + CNN). input processed selecting largest portion of each automated selection approach 64 × size utilized reduce computational complexity. Healthcare privacy issues addressed collaborative training across decentralized secure aggregation PHE, underscoring effectiveness this approach. Three used train local BYOL model, together optimizes central encoder. employed (updated CNN), resulting in accuracy 97.19%, precision 97.43%, recall 98.18%. reliability framework's performance demonstrated through statistical analysis five-fold cross-validation. efficacy proposed further showcased showing its on distinct modality datasets: skin cancer, breast chest X-rays. In conclusion, offers promising solution accurate diagnosis X-rays, preserving overcoming challenges scarcity

Language: Английский

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Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency

Future Internet, Journal Year: 2025, Volume and Issue: 17(1), P. 18 - 18

Published: Jan. 5, 2025

Federated Learning (FL) is a distributed machine-learning paradigm that enables models to be trained across multiple decentralized devices or servers holding local data without transferring the raw central location. However, applying FL heterogeneous IoT scenarios comes with several challenges due diverse nature of these in terms hardware capabilities, communications, and heterogeneity. Furthermore, conventional parameter server-based aggregates parameters directly, which incurs high communication overhead. To this end, paper designs hierarchical federated-learning framework for systems, focusing on enhancing efficiency ensuring security through lightweight encryption. By leveraging aggregation, stream encryption, adaptive device participation, proposed provides an efficient robust solution federated learning dynamic resource-constrained environments. The extensive experimental results show significantly reduces round time by 20%.

Language: Английский

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SEPPDL: A Secure and Efficient Privacy-Preserving Deep Learning Inference Framework for Autonomous Driving

ACM Transactions on Autonomous and Adaptive Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

The autonomous driving system necessitates using privacy-preserving deep learning (PPDL) technologies as the safety assurance for its extensive application. However, existing PPDL solutions depend on intricate protocol designs robust security. Although leveraging advanced dedicated hardware platforms can significantly improve inference efficiency, frameworks that make best use of platform computility are scarce. Thus, balancing efficiency and security in remains an open question. This study presents SEPPDL, a secure tripartite framework based secret-sharing to balance privacy computational efficiency. We reduce communication calculation time by designing quantisation representation scheme, two new protocols, computation library utilises integer units GPU. experimental results show compared with state-of-the-art frameworks, SEPPDL reduces delay model 1/2 1/3 optimal while maintaining accuracy inference. Meanwhile, achieves 10-fold performance improvement lightweight model. As scale increases, SEPPDL-based even 86-fold VGG16.

Language: Английский

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A Differential Privacy Framework with Adjustable Efficiency–Utility Trade-Offs for Data Collection

Mathematics, Journal Year: 2025, Volume and Issue: 13(5), P. 812 - 812

Published: Feb. 28, 2025

The widespread use of mobile devices has led to the continuous collection vast amounts user-generated data, supporting data-driven decisions across a variety fields. However, growing volume these data raises significant privacy concerns, especially when they include personal information vulnerable misuse. Differential (DP) emerged as prominent solution enabling for decision-making while protecting user privacy. Despite their strengths, existing DP-based frameworks are often faced with trade-off between utility and computational overhead. To address challenges, we propose differentially private fractional coverage model (DPFCM), framework that adaptively balances overhead according requirements decisions. DPFCM introduces two parameters, α β, which control fractions collected elements respectively, ensure both diversity representative coverage. In addition, probability-based methods effectively determining minimum each should provide satisfy requirements. Experimental results on real-world datasets validate effectiveness DPFCM, demonstrating its high efficiency, applications requiring real-time decision-making.

Language: Английский

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Human Immune System Inspired Security for Federated Learning-Empowered Internet of Things

ACM Transactions on Internet of Things, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

The emergence of the Internet Things (IoT) has revolutionized service automation, enabling development smart applications. However, vast interconnectivity IoT devices not only produces large volumes data but also creates multiple potential attack surfaces. While Machine Learning (ML) offers insights from data, inherent privacy and security challenges hinder its effective utilization. Federated (FL) privacy-preserving ML for distributed edge devices. Nevertheless, susceptibility to attacks poses a threat integrity impacting services To tackle this challenge identify compromised by like label-flipped paper introduces an innovative defense mechanism modeled after human immune system. Analogous ‘B’ cells, which detect viruses within body, Reinforcement (RL) agent identifies malicious nodes that participate in federated learning enabled IoT. Subsequently, FL server, similar ‘T’ cells Human systems eliminate/destroy infected quarantines/discards (that are clients) their reported parameters. Like work together defend body against infections diseases, RL server defend/secure compromised/malicious Specifically, with help Deep (DRL), continually monitors model updates participating during training phase find then isolate or remove those (i.e., parameters) at while aggregating parameters global model. effectiveness proposed approach is demonstrated through experiments, where detects malicious/compromised discards such nodes. We evaluate our using numerical results obtained experiments we observe outperforms existing state-of-the-art approaches terms detection rate, error accuracy enhancing

Language: Английский

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A Symmetric Projection Space and Adversarial Training Framework for Privacy-Preserving Machine Learning with Improved Computational Efficiency

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3275 - 3275

Published: March 17, 2025

This paper proposes a data security training framework based on symmetric projection space and adversarial training, aimed at addressing the issues of privacy leakage computational efficiency encountered by current protection technologies when processing sensitive data. By designing new loss function combining autoencoders with proposed method effectively balances model utility. Experimental results show that, for financial time-series tasks, using achieves precision 0.95, recall 0.91, accuracy 0.93, significantly outperforming traditional cross-entropy loss. In image yields 0.90, mAP@50 mAP@75 0.91 respectively, demonstrating its strong advantage in complex tasks. Furthermore, experiments different hardware platforms (Raspberry Pi, Jetson, NVIDIA 3080 GPU) that performs well low-computation devices exhibits significant advantages high-performance GPUs, particularly terms efficiency, good scalability efficiency. The experimental validate superiority

Language: Английский

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Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration across Distributed Data Sources

Industrial & Engineering Chemistry Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

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A comprehensive survey on secure healthcare data processing with homomorphic encryption: attacks and defenses

Deleted Journal, Journal Year: 2025, Volume and Issue: 22(1)

Published: April 5, 2025

Language: Английский

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Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 16, 2025

Abstract In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores integration Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, leveraging FL’s decentralized architecture to enable collaborative model training across healthcare organizations without exposing raw patient data. To enhance privacy, DP injects statistical noise into updates made by model. mitigates adversarial attacks prevents data leakage. The proposed work uses Breast Cancer Wisconsin Diagnostic dataset address critical challenges such as heterogeneity, privacy-accuracy trade-offs, computational overhead. From experimental results, FL combined with achieves 96.1% accuracy a budget ε = 1.9, ensuring strong minimal performance trade-offs. comparison, traditional non-FL achieved 96.0% accuracy, but at cost requiring centralized storage, which poses significant risks. These findings validate feasibility privacy-preserving artificial intelligence models in real-world clinical applications, effectively balancing protection reliable medical predictions.

Language: Английский

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