Hierarchical DICOM Data Masking for Privacy-Safe Image Sharing
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Solution Overview
Problem
The safe dissemination of medical image data in the digital age is hindered by the need for effective masking of sensitive and private patient information, which is often included in DICOM files, to protect privacy while facilitating sharing and usage in medical teaching, research, and AI applications.
Innovation Solution
A system and method for data masking that involves obtaining original files, applying masking templates based on hierarchical relationships, and generating target files with flexible masking modes, ensuring privacy while maintaining data usability through format conversion and storage in shared spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensitive patient information is included in DICOM files for medical teaching and research, then data usability and accessibility are improved, but patient privacy protection deteriorates
Solution Approach 1:
The patent extracts sensitive patient information (such as patient names, IDs, and other personal identifiers) from DICOM files and replaces them with anonymized dummy data. This extraction approach allows the medical image data to remain usable for teaching and research while removing the harmful privacy risks associated with identifiable patient information.
Solution Approach 2:
The patent creates anonymized copies of original DICOM files by replacing sensitive information with dummy data while preserving the structural and diagnostic integrity of the medical images. These copied files can be freely shared and used for educational and research purposes without compromising patient privacy, as they contain no real identifiable information.
2Reliability
If data masking is applied to protect patient privacy, then privacy protection is improved, but data accessibility and usability may deteriorate
Solution Approach 1:
The patent applies masking selectively only to specific sensitive fields (such as patient name, ID, and other personal identifiers) while leaving the rest of the DICOM file structure and medical image data intact. This localized approach ensures that privacy protection is achieved without compromising the overall usability and accessibility of the medical data for its intended purposes.
Solution Approach 2:
The patent changes the parameter values of sensitive information fields by replacing them with anonymized dummy data, while maintaining the same data structure and file format. This parameter transformation approach ensures that the masked files remain compatible with existing medical software and systems, preserving data accessibility without sacrificing privacy protection.
3Adaptability or versatility
If multiple masking templates are created for different data types, then masking flexibility is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal masking system that can handle multiple types of sensitive information (patient names, IDs, addresses, etc.) using a unified set of masking templates and dummy data. This multi-functional approach allows the same masking infrastructure to serve various data types and masking requirements, achieving flexibility without proportionally increasing system complexity.
Data Source
AI summary
The present disclosure is related to systems and methods for data masking. The method includes obtaining at least one original file and a hierarchical relationship that is associated with data in the at least one original file. The method includes obtaining a masking template for the data in the at least one original file. The method includes masking the data in the at least one original file based on the masking template, to generate at least one target file. The method includes storing the at least one target file based on the hierarchical relationship.