[2603.22954] Privacy-Preserving EHR Data Transformation via Geometric Operators: A Human-AI Co-Design Technical Report

Computer Science > Cryptography and Security arXiv:2603.22954 (cs) [Submitted on 24 Mar 2026] Title:Privacy-Preserving EHR Data Transformation via Geometric Operators: A Human-AI Co-Design Technical Report Authors:Maolin Wang, Beining Bao, Gan Yuan, Hongyu Chen, Bingkun Zhao, Baoshuo Kan, Jiming Xu, Qi Shi, Yinggong Zhao, Yao Wang, Wei Ying Ma, Jun Yan View a PDF of the paper titled Privacy-Preserving EHR Data Transformation via Geometric Operators: A Human-AI Co-Design Technical Report, by Maolin Wang and 11 other authors View PDF Abstract:Electronic health records (EHRs) and other real-world clinical data are essential for clinical research, medical artificial intelligence, and life science, but their sharing is severely limited by privacy, governance, and interoperability constraints. These barriers create persistent data silos that hinder multi-center studies, large-scale model development, and broader biomedical discovery. Existing privacy-preserving approaches, including multi-party computation and related cryptographic techniques, provide strong protection but often introduce substantial computational overhead, reducing the efficiency of large-scale machine learning and foundation-model training. In addition, many such methods make data usable for restricted computation while leaving them effectively invisible to clinicians and researchers, limiting their value in workflows that still require direct inspection, exploratory analysis, and human interpretation. We prop...