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Found 5 papers, 3 papers with code
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security.
Node Injection Link Stealing Attack
Furthermore, we examine the application of differential privacy (DP) mechanisms to mitigate the impact of our proposed attack, we analyze the trade-off between privacy preservation and model utility.
Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics
In this paper, we present an adversarial Auto-Encoder--based approach to hide gender-related information in speaker embeddings, while preserving their effectiveness for speaker verification.
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security.
Privacy Preserving Image Registration
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame.
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Cubic splines Image Registration
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Affine Image Registration
Cubic splines Image Registration
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