Alert Data Transformation for Privacy-Preserving ML Training
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Solution Overview
Problem
Existing methods for training machine learning models to predict alerts and incidents on enterprise software platforms face challenges due to the inclusion of user-generated content (UGC), which is subject to privacy regulations, making it difficult to utilize UGC data for accurate model training while ensuring compliance.
Innovation Solution
The solution involves transforming UGC data by segregating monitoring service alerts into problem and auxiliary details components, applying feature extraction, and performing non-linear transformations to generate UGC transformed alert data, allowing the model to utilize anonymized UGC without compromising privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user generated content (UGC) data is used to train machine learning models for accurate alert prediction, then model accuracy and predictive capability are improved, but privacy compliance and data security are compromised
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) and sensitive UGC data from alert messages before using the data for model training. A semantic parser identifies and extracts PII entities such as names, email addresses, and phone numbers, separating them from the core alert content that is needed for training.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw UGC data into anonymized representations. This intermediary system applies privacy-preserving transformations including entity anonymization, data perturbation, and differential privacy techniques, allowing the model to learn from UGC patterns without direct access to sensitive personal information.
2Object-affected harmful factors
If UGC data is anonymized and transformed to protect privacy, then privacy compliance is improved, but data quality and model training effectiveness may deteriorate
Solution Approach 1:
The patent performs preliminary privacy-preserving transformations on UGC data before the data is used for model training. By pre-anonymizing the data and preserving useful patterns through careful transformation, the system ensures that privacy compliance is established upfront while maintaining the data's training value for alert prediction.
Solution Approach 2:
The patent applies parameter changes to the data through various transformation techniques including data perturbation, adding noise, and modifying data representations. These parameter changes preserve the statistical patterns and relationships needed for model training while ensuring that individual identifiable information is obscured and privacy requirements are met.
3Productivity
If semantic parsing and feature extraction are applied to segregate alert components, then data processing capability is improved, but computational complexity increases
Solution Approach 1:
The patent segments alert messages into distinct components using semantic parsing, separating the core alert content from auxiliary details and PII. This segmentation is achieved through rule-based parsers and machine learning models that identify and extract different entity types, enabling targeted processing of each component according to its specific requirements.
Solution Approach 2:
The patent applies different processing techniques to different parts of the alert data based on their specific characteristics. For example, PII entities receive anonymization processing, while core alert messages receive feature extraction for model training. This local quality approach optimizes processing effectiveness for each data type while managing overall computational complexity.
Data Source
AI summary
Apparatus, methods, and computer program products for generating UGC transformed alert data from a monitoring service alert are provided. An apparatus may include program code configured to cause the apparatus to retrieve a monitoring service alert, including a text string and user generated content (UGC) text. In addition, the example apparatus may be configured to programmatically parse the text string of the monitoring service alert to segregate the monitoring service alert into an alert message problem component and an alert auxiliary details component. Further, the apparatus may be configured to generate an alert message problem embedding and an alert message description embedding by applying feature extraction to the alert message problem component and the alert auxiliary details component, respectively. The example apparatus may further be configured to output UGC transformed alert data based on the alert message problem embedding and the alert message description embedding.