Paper
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
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 propose a real-world-data transformation framework for privacy-preserving sharing of structured clinical records. Instead of converting data into opaque representations, our approach constructs transformed numeric views that preserve medical semantics and major statistical properties while, under a clearly specified threat model, provably breaking direct linkage between those views and protected patient-level attributes. Through collaboration between computer scientists and the AI agent \textbf{SciencePal}, acting as a constrained tool inventor under human guidance, we design three transformation operators that are non-reversible within this threat model, together with an additional mixing strategy for high-risk scenarios, supported by theoretical analysis and empirical evaluation under reconstruction, record linkage, membership inference, and attribute inference attacks.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.22954v1</id>\n <title>Privacy-Preserving EHR Data Transformation via Geometric Operators: A Human-AI Co-Design Technical Report</title>\n <updated>2026-03-24T08:49:22Z</updated>\n <link href='https://arxiv.org/abs/2603.22954v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.22954v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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 propose a real-world-data transformation framework for privacy-preserving sharing of structured clinical records. Instead of converting data into opaque representations, our approach constructs transformed numeric views that preserve medical semantics and major statistical properties while, under a clearly specified threat model, provably breaking direct linkage between those views and protected patient-level attributes. Through collaboration between computer scientists and the AI agent \\textbf{SciencePal}, acting as a constrained tool inventor under human guidance, we design three transformation operators that are non-reversible within this threat model, together with an additional mixing strategy for high-risk scenarios, supported by theoretical analysis and empirical evaluation under reconstruction, record linkage, membership inference, and attribute inference attacks.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CR'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-24T08:49:22Z</published>\n <arxiv:primary_category term='cs.CR'/>\n <author>\n <name>Maolin Wang</name>\n </author>\n <author>\n <name>Beining Bao</name>\n </author>\n <author>\n <name>Gan Yuan</name>\n </author>\n <author>\n <name>Hongyu Chen</name>\n </author>\n <author>\n <name>Bingkun Zhao</name>\n </author>\n <author>\n <name>Baoshuo Kan</name>\n </author>\n <author>\n <name>Jiming Xu</name>\n </author>\n <author>\n <name>Qi Shi</name>\n </author>\n <author>\n <name>Yinggong Zhao</name>\n </author>\n <author>\n <name>Yao Wang</name>\n </author>\n <author>\n <name>Wei Ying Ma</name>\n </author>\n <author>\n <name>Jun Yan</name>\n </author>\n </entry>"
}