AIMC Topic: Privacy

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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 ...
Humans Artificial Intelligence Confidentiality Privacy Computer Security Delivery of Health Care Ophthalmology
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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...
Imagination Algorithms Electroencephalography Brain-Computer Interfaces Humans Deep Learning Privacy Computer Security 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 Online Systems Algorithms Transfer, Psychology Electroencephalography Humans Adult 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 ...
Privacy Resource Allocation Machine Learning Humans Algorithms Neural Networks, Computer
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Robust and Privacy-Preserving Decentralized Deep Federated Learning Training: Focusing on Digital Healthcare Applications.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machine learning, gaining widespread attention due to its ability to update parameters without collecting raw data from users, especially in digital health...
Humans Neural Networks, Computer Deep Learning Privacy Computer Security Algorithms Delivery of Health Care
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Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

Journal of medical Internet research Aug 1, 2024
This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of ...
Privacy Education, Medical Artificial Intelligence Language Humans
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Subgraph-level federated graph neural network for privacy-preserving recommendation with meta-learning.

Neural networks : the official journal of the International Neural Network Society Jul 25, 2024
Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework al...
Privacy Computer Security Software Algorithms Neural Networks, Computer Humans Machine Learning
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Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth.

Annual review of biomedical data science Jul 24, 2024
Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data s...
Privacy Computer Security Humans Artificial Intelligence Data Science Confidentiality Biomedical Research
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Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption.

PloS one Jul 22, 2024
The widespread adoption of cloud computing necessitates privacy-preserving techniques that allow information to be processed without disclosure. This paper proposes a method to increase the accuracy and performance of privacy-preserving Convolutional...
Algorithms Privacy Cloud Computing Computer Security Humans Neural Networks, Computer
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Navigating New Legal Guardrails and Emerging AI Challenges.

The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics Jul 12, 2024
Here, we analyze the public health implications of recent legal developments - including privacy legislation, intergovernmental data exchange, and artificial intelligence governance - with a view toward the future of public health informatics and the...
Privacy United States Humans Public Health Informatics Artificial Intelligence Confidentiality Public Health
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