AIMC Topic: Privacy

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The urgent need to accelerate synthetic data privacy frameworks for medical research.

The Lancet. Digital health Nov 26, 2024
Synthetic data, generated through artificial intelligence technologies such as generative adversarial networks and latent diffusion models, maintain aggregate patterns and relationships present in the real data the technologies were trained on withou...
Biomedical Research Confidentiality Privacy Computer Security Humans Artificial Intelligence
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Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective.

Neural networks : the official journal of the International Neural Network Society Nov 20, 2024
Graph neural networks (GNNs) based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy ...
Algorithms Privacy Humans Neural Networks, Computer
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Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

IEEE journal of biomedical and health informatics Nov 6, 2024
In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac abnormalities from electrocardiogram (ECG) data. This approach allows medical institutions to collabor...
Databases, Factual Deep Learning Electrocardiography Humans Neural Networks, Computer Signal Processing, Computer-Assisted Privacy
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Noise-resistant sharpness-aware minimization in deep learning.

Neural networks : the official journal of the International Neural Network Society Oct 24, 2024
Sharpness-aware minimization (SAM) aims to enhance model generalization by minimizing the sharpness of the loss function landscape, leading to a robust model performance. To protect sensitive information and enhance privacy, prevailing approaches add...
Deep Learning Privacy Neural Networks, Computer Humans Algorithms
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Privacy-Preserving Technology Using Federated Learning and Blockchain in Protecting against Adversarial Attacks for Retinal Imaging.

Ophthalmology Oct 16, 2024
PURPOSE: Collaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy-enhancing technology that allows collaboration while respecting privacy via the development of models withou...
Humans Tomography, Optical Coherence Retina Artificial Intelligence Blockchain Computer Security Retrospective Studies Federated Learning Privacy Macular Degeneration
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MemberShield: A framework for federated learning with membership privacy.

Neural networks : the official journal of the International Neural Network Society Oct 1, 2024
Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keeping data on their premises. Even though FL offers privacy-by-design, it is vulnerable to membership ...
Algorithms Deep Learning Computer Security Privacy Humans Neural Networks, Computer
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Towards regulatory generative AI in ophthalmology healthcare: a security and privacy perspective.

The British journal of ophthalmology Sep 20, 2024
As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI ...
Privacy Humans Computer Security Ophthalmology Artificial Intelligence Confidentiality Delivery of Health Care
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Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 18, 2024
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this ch...
Electroencephalography Privacy Imagination Brain-Computer Interfaces Computer Security Algorithms Humans Deep Learning Machine Learning
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Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 26, 2024
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...
Machine Learning Algorithms Humans Online Systems Electroencephalography Adult Transfer, Psychology Privacy Male Brain-Computer Interfaces
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Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.

Sensors (Basel, Switzerland) Aug 8, 2024
Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and ...
Machine Learning Algorithms Resource Allocation Privacy Humans Neural Networks, Computer
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