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David Jules Froelicher

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David Jules Froelicher

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System and method for privacy-preserving distributed training of neural network models on distributed datasets

Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing