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A security scheme for distributing analysis codes supporting CDM-based research in a multi-center environment.

Seungho Jeon1, Chobyeol Shin1, Eunnarae Ko1

  • 1Graduate School of Information Security, Korea University, 145, Anrm-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

Computer Methods and Programs in Biomedicine
|October 16, 2022
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Summary
This summary is machine-generated.

Researchers can now securely access and analyze hospital Common Data Model (CDM) data remotely. This automated security framework enables data-driven research across institutions, enhancing medical data accessibility.

Keywords:
Common data modelKerberosMulti-centered medical researchSecure distribution-protocol

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Area of Science:

  • Medical Informatics
  • Cybersecurity
  • Data Science

Background:

  • Standardized medical data (Common Data Model - CDM) is often stored on-premises, limiting researcher access.
  • De-identification of patient data in CDM aids privacy but doesn't solve accessibility issues.

Purpose of the Study:

  • To enable secure, remote access and analysis of CDM data for researchers outside hospital networks.
  • To propose an automated security framework for multi-institutional, data-driven research using public networks.

Main Methods:

  • Developed a cryptographic scheme for secure code delivery and execution of analysis on institutional servers.
  • Implemented a multi-phase process: authentication, code signing, ticket issuing, and result distribution.
  • Utilized a group of servers for authentication, signing, ticket granting, relaying, and data analysis.

Main Results:

  • The proposed scheme allows authenticated researchers to securely submit analysis code.
  • Institutional servers automatically execute code and return results, ensuring data security.
  • Formal verification using BAN logic confirmed the scheme meets security objectives.

Conclusions:

  • The automated security framework successfully enables secure, remote analysis of CDM data.
  • The scheme is verified to achieve its security goals through BAN logic proof.
  • Applicable to similar systems requiring secure distributed data analysis.