On-the-fly Anonymization Module for N-Tier Application Performance Testing
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Solution Overview
Problem
Current performance testing methods for n-tier applications cannot utilize production data effectively due to confidentiality concerns, leading to complex and time-consuming dataset creation and inefficient anonymization solutions that do not guarantee data confidentiality or consistency.
Innovation Solution
A method and system for on-the-fly anonymization of production data, using an anonymization module that processes data in real-time, ensuring sensitive information is encoded and maintained for performance analysis while preserving data confidentiality, allowing for complete dataset usage in performance testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If production data is used for performance testing, then testing accuracy and realism are improved, but data confidentiality is compromised
Solution Approach 1:
The patent introduces an anonymization module as an intermediary component between the production data source and the performance testing system. This module automatically masks sensitive data fields while preserving the structural and functional characteristics of the data, allowing realistic testing without exposing confidential information. The anonymization module acts as a mediator that transforms raw production data into testing-safe data that maintains authenticity for performance measurement purposes.
2Object-affected harmful factors
If test dataset is created to ensure data confidentiality, then data security is improved, but time consumption and complexity increase
Solution Approach 1:
The system performs preliminary anonymization processing of production data before it is used for performance testing. The anonymization module pre-processes the data by identifying and masking sensitive fields according to predefined confidentiality rules, creating a ready-to-use anonymized dataset. This preliminary action eliminates the need for manual data preparation and ensures confidentiality is maintained from the outset, significantly reducing the time and complexity associated with creating test datasets.
Solution Approach 2:
The anonymization module operates autonomously to transform production data into anonymized test data without requiring manual intervention. The system automatically applies masking rules, identifies sensitive data patterns, and generates anonymized datasets that are immediately ready for performance testing. This self-service capability eliminates the need for manual data preparation workflows, reducing both time consumption and operational complexity while ensuring consistent application of confidentiality standards.
3Object-affected harmful factors
If existing anonymization solutions are used, then data masking is achieved, but processing overhead and response time increase
Solution Approach 1:
The patent replaces traditional manual or batch-based anonymization processes with an automated, real-time anonymization module that integrates directly into the performance testing workflow. Instead of using complex external tools or manual masking procedures, the system employs an embedded anonymization engine that processes data on-the-fly during testing operations. This substitution of mechanical/manual processes with an automated computational system significantly reduces processing overhead and response time while maintaining confidentiality standards.
4Object-affected harmful factors
If complete production data is anonymized, then confidentiality is ensured, but data consistency and testing realism are compromised
Solution Approach 1:
The anonymization module applies selective masking to specific sensitive data fields while leaving other non-sensitive fields unchanged. Instead of uniformly anonymizing all data, the system identifies and masks only the confidential portions (such as personal identifiers, sensitive business information) while preserving the overall data structure, relationships, and characteristics needed for realistic performance testing. This local quality approach ensures confidentiality where needed while maintaining data consistency and testing realism in other areas.
Data Source
AI summary
A device and method for analyzing a performance of an n-tier application capable of carrying out on-the-fly anonymization processing of production data. The production data is generated following a performance test request message transmitted to the n-tier application. The anonymization processing is implemented by an anonymization module that identifies, from a sensitive data identification repository, data to be anonymized in the response message. The anonymization processing also includes generating, from an anonymization repository, anonymized data from the previously identified data to be anonymized, and generating an anonymized response message from the anonymized data and the response message.