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ARX--A Comprehensive Tool for Anonymizing Biomedical Data.

Fabian Prasser1, Florian Kohlmayer1, Ronald Lautenschläger1

  • 1Technische Universität München, München, Germany.

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Protecting sensitive biomedical data requires balancing privacy with data utility. ARX is a new anonymization tool offering efficient privacy methods, an intuitive interface, and robust support for researchers.

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Area of Science:

  • Biomedical Informatics
  • Data Privacy
  • Statistical Disclosure Control

Background:

  • Biomedical research increasingly relies on collaboration and data sharing.
  • Linking sensitive data from distributed sources poses significant privacy risks.
  • Effective statistical disclosure control is crucial for protecting sensitive information.

Purpose of the Study:

  • To address the need for tools that balance data privacy with utility in biomedical research.
  • To present ARX, a novel anonymization tool designed for researchers and data stewards.
  • To provide an overview of the anonymization process with intuitive and replicable methods.

Main Methods:

  • Implementation of a wide variety of privacy methods with high efficiency.
  • Development of an intuitive, cross-platform graphical user interface (GUI).
  • Provision of a programming interface for integration into existing software systems.

Main Results:

  • ARX offers a comprehensive suite of anonymization techniques.
  • The tool provides an accessible and user-friendly interface for managing data privacy.
  • ARX is well-documented and actively supported, addressing limitations of existing software.

Conclusions:

  • ARX facilitates secure data sharing in biomedical research by providing robust anonymization capabilities.
  • The tool's design emphasizes usability, efficiency, and community support.
  • ARX represents a significant advancement in tools for statistical disclosure control in sensitive data environments.