Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease

Ines N. Duarte; Praphulla M. S. Bhawsar; Lee K. Mason; Jeya Balaji Balasubramanian; Daniel E. Russ; Arlindo L. Oliveira; Jonas S. Almeida

cs.LGFeb 26, 2026

Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease

Authors: Ines N. Duarte, Praphulla M. S. Bhawsar, Lee K. Mason, Jeya Balaji Balasubramanian, Daniel E. Russ, Arlindo L. Oliveira, Jonas S. Almeida

Organizations: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, United States · INESC-ID, R. Alves Redol 9, Lisbon 1000-029, Portugal · Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon 1049-001, Portugal

Abstract

A recent report on "Learning the natural history of human disease with generative transformers" created an opportunity to assess the engineering challenge of delivering user-facing Generative AI applications in privacy-sensitive domains. The application of these models, particularly for personalized healthcare tasks like predicting individual morbidity risk, is typically constrained by data privacy concerns. This project was accordingly designed as an in-browser model deployment exercise (an "App") testing the architectural boundaries of client-side inference generation (no downloads or installations). We relied exclusively on the documentation provided in the reference report to develop the model, specifically testing the "R" component of the FAIR data principles: Findability, Accessibility, Interoperability, and Reusability. The successful model deployment, leveraging ONNX and a custom JavaScript SDK, establishes a secure, high-performance architectural blueprint for the future of private generative AI in medicine.

Privacy-Preserving Language Models

Privacy-preserving language models protect sensitive data during training, inference, personalization, and information sharing.

See all 178 papers

Key-Embedded Privacy for Decentralized AI in Biomedical Omics
Mar 30, 2026Rongyu Zhang, Hongyu Dong, Gaole Dai +13Privacy-Preserving ML Federated Learning
School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, 210023, China. · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, 999077, China. · State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, 100871, China. +8
Automated screening of privacy risks in medical AI
Oct 21, 2026Konstantinos Vrettos, Eleftherios Tzanis, Haridimos Kondylakis +3Membership Inference Attacks Privacy-Preserving ML
Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece · Division of Radiology, Department of Clinical Science Intervention and Technology (CLINTEC), Karolinska Institute, Huddinge, Sweden · Computer Science Department, University of Crete, Crete, Greece +4
Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Sep 29, 2026Chaoyu Zhang, Shanghao Shi, Heng Jin +3Privacy-Preserving Federated Learning Model Inversion Attacks
Virginia Tech · Washington University in St. Louis · University of South Florida

Mar 30, 2026J/K move · Enter open · S save

Key-Embedded Privacy for Decentralized AI in Biomedical Omics

Rongyu Zhang, Hongyu Dong, Gaole Dai, Ziqi Qiao+12

School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, 210023, China. · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, 999077, China. · State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, 100871, China.+8

Privacy-Preserving ML Federated Learning Implicit Neural Representations

Open paper AskPDF arXiv

The rapid adoption of data-driven methods in biomedicine has intensified concerns over privacy, governance, and regulation, limiting raw data sharing and hindering the assembly of representative cohorts for clinically relevant AI. This landscape necessitates practical, efficient privacy solutions, as cryptographic defenses often impose heavy overhead and differential privacy can degrade performance, leading to sub-optimal outcomes in real-world settings. Here, we present a lightweight federated learning method, INFL, based on Implicit Neural Representations that addresses these challenges. Our approach integrates plug-and-play, coordinate-conditioned modules into client models, embeds a secret key directly into the architecture, and supports seamless aggregation across heterogeneous sites. Across diverse biomedical omics tasks, including cohort-scale classification in bulk proteomics, regression for perturbation prediction in single-cell transcriptomics, and clustering in spatial transcriptomics and multi-omics with both public and private data, we demonstrate that INFL achieves strong, controllable privacy while maintaining utility, preserving the performance necessary for downstream scientific and clinical applications.

Abstract

Privacy-Preserving Language Models

Explore similar work

Key-Embedded Privacy for Decentralized AI in Biomedical Omics

Automated screening of privacy risks in medical AI

Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning