Publications

Privacy-Enhancing Technologies in Biomedical Data Science

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for …

H. Cho, David Froelicher, N. Dokmai, A. Nandi, S. Sadhuka, M. Man-Hou Hong, B. Berger

Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets

M. Hong, David Froelicher, R. Magner, V. Popic, B. Berger, H. Cho

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

Sharing data across multiple institutions for genome-wide association studies (GWAS) would enable discovery of novel genetic variants …

H. Cho, David Froelicher, J. Chen, M. Edupalli, A. Pyrgelis, J. R. Troncoso-Pastoriza, J.-P. Hubaux, B. Berger

Scalable and Privacy-Preserving Federated Principal Component Analysis

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the …

David Froelicher, H. Cho, M. Edupalli, J. S. Sousa, J.-P. Bossuat, A. Pyrgelis, J. R. Troncoso-Pastoriza, J.-P. Hubaux, B. Berger

sfkit: A Web-Based Toolkit for Secure and Federated Genomic Analysis

Advances in genomics are increasingly depending upon the ability to analyze large and diverse genomic data collections, which are often …

S. Mendelsohn, David Froelicher, D. Loginov, D. Bernick, B. Berger, H. Cho

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities …

David Froelicher, J. R. Troncoso-Pastoriza, J. L. Raisaro, M. Cuendet, J. S. Sousa, H. Cho, B. Berger, J. Fellay, J.-P. Hubaux

Scalable Privacy-Preserving Distributed Learning

In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by …

David Froelicher, J. R. Troncoso-Pastoriza, A. Pyrgelis, S. Sav, J. S. Sousa, J.-P. Bossuat, J.-P. Hubaux

POSEIDON: Privacy-Preserving Federated Neural Network Learning

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated …

S. Sav, A. Pyrgelis, J. R. Troncoso-Pastoriza, David Froelicher, J.-P. Bossuat, J. S. Sousa, J.-P. Hubaux

Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing …

M. Kim, A.Harmanci, J.-P. Bossuat, S. Carpov, J. H. Cheon, I. Chillotti, W. Cho, David Froelicher, N. Gama, M. Georgieva, S. Hong, J.-P. Hubaux, D. Kim, K. Lauter, Y. Ma, L. Ohno-Machado, H. Sofia, Y. Son, Y. Song, J. Troncoso-Pastoriza, X. Jiang

Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption

J.R. Troncoso-Pastoriza, David Froelicher, P. Hu, A. Aloufi, J.P. Hubaux

MedCo^2: Privacy-Preserving Cohort Exploration and Analysis

David Froelicher, M. Misbach, J. R. Troncoso-Pastoriza, J.L. Raisaro, J.-P. Hubaux

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

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical re- search, but remains …

David Froelicher, J.R. Troncoso-Pastoriza, J.S. Sousa, J.P. Hubaux

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be …

David Froelicher, P. Egger, J. S. Sousa, J. L. Raisaro, Z. Huang, C. Mouchet, B. Ford, J.-P. Hubaux

SPORT: Sharing Proofs of Retrievability across Tenants

Proofs of Retrievability (POR) are cryptographic proofs which provide assurance to a single tenant (who creates tags using his secret …

F. Armknecht, J.-M. Bohli, G. Karame, David Froelicher