Medical Dataset Anonymization Profiles for Secure Cloud Transfer
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Solution Overview
Problem
Medical facilities face challenges in securely and efficiently anonymizing patient data for transmission to external data centers due to varying data protection requirements and the risk of sensitive information leakage, especially when using cloud-based software solutions.
Innovation Solution
A method for transmitting medical datasets that involves providing a security profile to anonymize data elements based on their type, creating an anonymized dataset, and transmitting it to an external memory, ensuring only anonymized data is shared, with monitoring to prevent unauthorized data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all patient data is automatically transmitted to external memory without anonymization, then transmission efficiency is improved, but data protection is compromised
Solution Approach 1:
The system performs preliminary anonymization of patient data elements before transmission to the external memory. The anonymization process is automatically executed as a pre-processing step, removing or masking sensitive information such as patient identifiers while preserving clinically relevant data, thereby eliminating data protection risks before transmission occurs
Solution Approach 2:
The patent introduces an intermediary anonymization layer between the internal and external memory systems. This intermediary process transforms the original patient data into anonymized form, acting as a buffer that protects sensitive information while still allowing necessary data to be transmitted for cloud-based processing
2Object-affected harmful factors
If manual anonymization processes are used before uploading patient data, then data protection is improved, but transmission time is increased
Solution Approach 1:
The system implements self-service automated anonymization that operates without manual intervention. The anonymization process is automatically triggered when data is prepared for transmission, and the system autonomously identifies, selects, and applies appropriate anonymization methods based on data types and security profiles, eliminating time-consuming manual operations
Solution Approach 2:
The patent dynamically adjusts anonymization parameters based on data characteristics and security requirements. Different anonymization强度 (strengths) are applied to different data elements according to their sensitivity and clinical utility, optimizing the balance between data protection and transmission efficiency through automated parameter selection
3Manufacturing precision
If different anonymization rules are applied to different data types, then data protection precision is improved, but system complexity is increased
Solution Approach 1:
The system segments patient data into different data types (e.g., demographic information, clinical measurements, diagnostic results) and applies specific anonymization rules to each segment. This segmentation allows precise control over what information is protected and what is preserved for clinical use, with each data type receiving appropriate anonymization treatment
Solution Approach 2:
The patent implements a universal anonymization framework that handles multiple data types through a single integrated system. The system provides multi-functional capabilities by automatically adapting anonymization methods to different data types while maintaining a unified security profile management approach, reducing operational complexity despite handling diverse data requirements
Data Source
AI summary
One or more example embodiments of the present invention relates to a method for transmission of medical datasets, including providing a medical dataset from an internal data memory, the medical dataset including data elements, each data element of the data elements having a data type, providing a security profile, the security profile including rules for a degree of a data anonymization of data elements of a respective data type, creating an anonymized medical dataset based on the provided medical dataset and the provided security profile, the data elements of the medical dataset are each anonymized based on the respective data type and the security profile, the anonymized medical dataset includes the anonymized data elements and transmitting the anonymized medical dataset to an external data memory.