Towards secure big data analysis via fully homomorphic encryption algorithms
Privacy-preserving techniques allow private information to be used without compromising
privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) …
privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) …
Collaborative training of medical artificial intelligence models with non-uniform labels
S Tayebi Arasteh, P Isfort, M Saehn… - Scientific Reports, 2023 - nature.com
Due to the rapid advancements in recent years, medical image analysis is largely dominated
by deep learning (DL). However, building powerful and robust DL models requires training …
by deep learning (DL). However, building powerful and robust DL models requires training …
Accessibility of covariance information creates vulnerability in Federated Learning frameworks
Abstract Motivation Federated Learning (FL) is gaining traction in various fields as it enables
integrative data analysis without sharing sensitive data, such as in healthcare. However, the …
integrative data analysis without sharing sensitive data, such as in healthcare. However, the …
[HTML][HTML] Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution
F Manni, A Bukharev, A Jain, S Moorthy… - AMIA Annual …, 2022 - ncbi.nlm.nih.gov
Federated learning (FL) is a privacy preserving approach to learning that overcome issues
related to data access, privacy, and security, which represent key challenges in the …
related to data access, privacy, and security, which represent key challenges in the …
Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines
Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed
indefinitely, and affects the owner's family members as well. The recent large-scale genomic …
indefinitely, and affects the owner's family members as well. The recent large-scale genomic …