David Jules Froelicher

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Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the ...

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag"variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit ge ...

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditiona ...

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverag ...

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients i ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

Existing approaches to sharing of distributed medical data either provide only limited protection of patients' privacy or sacrifice the accuracy of results. Here, the authors propose a federated analytics system, based on multiparty homomorphic encryption ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

David Jules Froelicher

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Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

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

POSEIDON: Privacy-Preserving Federated Neural Network Learning

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

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

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

Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption

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

Scalable Privacy-Preserving Distributed Learning