AIMC Topic: Privacy

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Navigating autonomy, privacy, and ageism in robot home care with aged users: A preliminary analysis of ROB-IN.

Bioethics
In this article, I propose an ethical analysis of assistive domestic robots for older users. In doing so, I illustrate my inquiry with the example of ROB-IN assistive robot. ROB-IN is a Spanish project which is devoted to developing a robot that will...

Self-Supervised WiFi-Based Identity Recognition in Multi-User Smart Environments.

Sensors (Basel, Switzerland)
The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often...

Context-Contingent Privacy Concerns and Exploration of the Privacy Paradox in the Age of AI, Augmented Reality, Big Data, and the Internet of Things: Systematic Review.

Journal of medical Internet research
BACKGROUND: Despite extensive research into technology users' privacy concerns, a critical gap remains in understanding why individuals adopt different standards for data protection across contexts. The rise of advanced technologies such as the Inter...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Briefings in bioinformatics
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly ...

The INNOVATE framework to foster ethics of artificial intelligence.

Recenti progressi in medicina
ChatGPT, the latest advancement in Artificial Intelligence (AI), represents one of the most advanced and rapidly evolving chatbot technologies to date. Its capability to provide swift and intelligent responses has garnered admiration from scientists ...

Enhancing Privacy-Preserving Cancer Classification with Convolutional Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating primary tumor location into the diagnostic process greatly improves survival rates. However, traditional...

Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

Studies in health technology and informatics
INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exceptio...

Assessing the Impact of Federated Learning and Differential Privacy on Multi-centre Polyp Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Federated Learning (FL) is emerging in the medical field to address the need for diverse datasets while complying with data protection regulations. This decentralised learning paradigm allows hospitals (clients) to train machine learning models local...

Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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 fe...

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of...