Interface Code Anonymization for Transaction Data Security
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Solution Overview
Problem
In transaction processing systems, especially those using mainframe computers, manually analyzing and modifying source code to anonymize sensitive data fields is labor-intensive, error-prone, and challenging due to complexity and the need to understand inter-dependencies between program components, making it difficult to ensure secure data protection without compromising system functionality.
Innovation Solution
The implementation of an interface code modification system that includes an analysis stage where users select data fields for anonymization through a user interface, adding indicators to the interface code rather than the program source code, and a runtime stage where anonymization is applied without modifying the program, ensuring secure data handling without altering the program logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis and modification of source code is performed to anonymize sensitive data fields, then data security is improved, but labor intensity and error rate increase
Solution Approach 1:
The system performs preliminary analysis of source code to automatically identify sensitive data fields before the anonymization process. By pre-configuring sensitivity rules and using static analysis techniques, the system prepares the anonymization plan in advance, eliminating the need for manual field-by-field analysis and reducing labor intensity while maintaining security standards.
Solution Approach 2:
The anonymization system is designed to automatically execute the anonymization process without requiring manual intervention. Once sensitive fields are identified through static analysis, the system self-applies anonymization transformations, generates updated code, and validates results automatically, thereby improving productivity while ensuring consistent security application.
2Reliability
If manual modification of source code is performed to anonymize data fields, then data protection is improved, but error rate and complexity increase
Solution Approach 1:
The system extracts the anonymization logic from manual source code modification processes and implements it as an automated analysis and transformation system. By separating field identification, anonymization rule application, and code generation into distinct automated components, the system reduces errors while managing complexity through modular architecture rather than manual coordination.
3Adaptability or versatility
If indicators are added to interface code instead of program source code, then functional equivalence is maintained, but data security requirements may be compromised
Solution Approach 1:
The system uses interface code as an intermediary layer between the program source code and the external environment. By adding indicators to interface code that reference sensitive data fields, the system enables anonymization at the data output point without modifying the core program logic. This intermediary approach maintains functional equivalence while implementing security requirements through a separate control mechanism.
Data Source
AI summary
In some examples, a system renders, in a user interface, data fields of an interface code, the interface code to input or output the data fields in a transaction, and receives a selection, made in the user interface, of a first data field from among the data fields of the interface code. The system modifies the interface code by adding an indicator associated with the first data field, the indicator specifying anonymization of the first data field during the transaction that uses the first data field, the modifying of the interface code producing a modified interface code.