Publications

CL-DLO: Direct Centerline Prediction for Instance Segmentation of Deformable Linear Objects in Confined Environments

Instance segmentation of cables in telecommunication ducts supports robotic inspection and maintenance by enabling individual cables to …

R. Garcia Bustillos, P. Amargant I Alvarez, K. Walker, J. Hughes, David Froelicher

CAPMAS: Capability-Based Delegation of Privileges in Multi-Agent Systems

Agentic systems require secure and efficient delegation of privileges across multiple collaborating agents. Existing approaches fall …

R. Veski, R. Guerraoui, David Froelicher

Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption

We present Shechi, an easy-to-use programming framework for secure high-performance computing on distributed datasets. Shechi …

H. Smajlovic, David Froelicher, A. Shajii, B. Berger, H. Cho, I. Numanagic

Design Patterns for Securing LLM Agents against Prompt Injections

As AI agents, powered by Large Language Models (LLMs), become increasingly versatile and capable of addressing a broad spectrum of …

L. Beurer-Kellner, B. Buesser, A.-M. Creţu, E. Debenedetti, D. Dobos, D. Fabian, M. Fischer, David Froelicher, K. Grosse, D. Naeff, E. Ozoani, A. Paverd, F. Tramèr, V. Volhejn

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

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

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 …