Anonymizing VSAM Data Sets for Diagnosis
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Solution Overview
Problem
In the z/OS operating system, data sets with index and data components pose challenges in data privacy and compliance due to the need to analyze both components to identify corruption, which is hindered by increasing concerns over personal and confidential information and regulatory restrictions.
Innovation Solution
A method for anonymizing data sets by creating a copy, removing personal information from the data component, and modifying index component key values with random values, while maintaining the data set structure, using an anonymizer that includes an index analyzer, redactor, key modifier, and index builder to synchronize and rebuild the index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the VSAM data set is turned over to be analyzed by a third party to identify corruption, then the corruption source can be determined, but personal and confidential information may become public
Solution Approach 1:
The patent creates a copy of the VSAM data set before analysis. The copy contains anonymized index keys (replaced with random values) while the original data component remains intact. This allows third-party analysis of the data component for corruption identification without exposing personal information through the index keys.
Solution Approach 2:
The patent extracts and removes personal information from the index component by replacing index keys with random values. This separation allows the data component to be analyzed independently without the personal identifying information that would normally link records to individuals.
2Object-affected harmful factors
If personal information is removed from the data set to protect privacy, then privacy concerns are addressed, but the ability to identify record locations is lost
Solution Approach 1:
The patent creates a modified copy where index keys are replaced with random values. These random values serve as anonymous identifiers that maintain the structural relationship between index and data components, allowing record location identification without using personal information.
Solution Approach 2:
The patent changes the parameter of index keys from personal information to random values. This parameter transformation maintains the functional role of keys in locating records while eliminating the privacy risk associated with personal identifying information.
3Object-affected harmful factors
If the index component is modified with random values to anonymize data, then privacy is protected, but the data set structure may be corrupted
Solution Approach 1:
The patent works on a copy of the data set rather than the original. The anonymization process modifies the index component of the copy by replacing keys with random values, while maintaining the overall VSAM data set structure and the relationship between index and data components.
Solution Approach 2:
The patent uses random values as intermediary placeholders that maintain the structural function of index keys without containing personal information. These random values act as mediators that preserve the data set structure while enabling privacy protection.
Data Source
AI summary
Apparatuses, methods, and computer program products are provided for anonymizing a data set having an index component and a data component. In certain embodiments, the apparatuses, methods and computer program products make a copy of the data set, removes personal information from the data component, modifies an index component key value with a random value, modifies a data component key value with the same random value, and maintains a data set structure of the index component and the data component.