AIMC Topic: Privacy

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A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.

Scientific reports
Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. However, sharing sensitive raw medical data with third parties for analysi...

Domain-incremental white blood cell classification with privacy-aware continual learning.

Scientific reports
White blood cell (WBC) classification plays a vital role in hematology for diagnosing various medical conditions. However, it faces significant challenges due to domain shifts caused by variations in sample sources (e.g., blood or bone marrow) and di...

Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.

PloS one
This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP), for diagnosing arrhythmia from 12-lead electrocardiogram (ECG) signals. The architecture supports ...

Artificial Intelligence-Enabled Facial Privacy Protection for Ocular Diagnosis: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Facial biometric data, while valuable for clinical applications, poses substantial privacy and security risks.

Promising for patients or deeply disturbing? The ethical and legal aspects of deepfake therapy.

Journal of medical ethics
Deepfakes are hyper-realistic but fabricated videos created with the use of artificial intelligence. In the context of psychotherapy, the first studies on using deepfake technology are emerging, with potential applications including grief counselling...

A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.

Scientific reports
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, comp...

An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data.

Scientific reports
With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy prese...

Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

Computers in biology and medicine
The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (ele...

A combined approach of evolutionary game and system dynamics for user privacy protection in human intelligence interaction.

Scientific reports
The rapid development of generative artificial intelligence (GenAI) has generated significant economic and social value, alongside risks to user privacy. For this purpose, this study investigates privacy protection in human-AI interaction by employin...

Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.

Computer methods and programs in biomedicine
Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys ...