AIMC Topic: Privacy

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Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.

JMIR mental health Dec 23, 2024
BACKGROUND: The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect wi...
Machine Learning Female Humans Stress, Psychological Wearable Electronic Devices Emotions Adult Male Privacy
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Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation.

Sensors (Basel, Switzerland) Dec 19, 2024
Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and...
Transportation Deep Learning Safety Privacy Video Recording Humans Artificial Intelligence
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Evaluation of privacy protection methods of public service advertising visual design in the perspective of artificial intelligence internet of things.

PloS one Dec 2, 2024
With the widespread application of monitoring technology and network storage technology, there are some privacy issues in the process of public service advertising visual design and the process of pushing public service advertising visual design work...
Computer Security Privacy Humans Advertising Internet of Things Artificial Intelligence
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Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.

Transfusion Nov 29, 2024
BACKGROUND: Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integra...
Humans Transfusion Medicine Federated Learning Blood Transfusion Artificial Intelligence Privacy Precision Medicine Information Dissemination
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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 Humans Privacy 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...
Signal Processing, Computer-Assisted Privacy Humans Neural Networks, Computer Deep Learning Electrocardiography Databases, Factual
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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...
Privacy Algorithms Deep Learning Humans Neural Networks, Computer
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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...
Tomography, Optical Coherence Retina Artificial Intelligence Retrospective Studies Computer Security Blockchain Federated Learning Privacy Macular Degeneration Humans
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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 ...
Deep Learning Privacy Computer Security Neural Networks, Computer Algorithms Humans
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