Huber Loss Estimator for Secure Predictive Data Models
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Solution Overview
Problem
Machine learning models used in predictive modeling are prone to malfeasant manipulation of training data, leading to reduced accuracy.
Innovation Solution
A system utilizing a Huber loss estimator to validate predictive outputs by comparing them with historical data, employing a pseudo free model to revalidate deviations, and taking actions such as altering training data or generating reports to ensure data models are secure from manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training data is used directly for predictive modeling, then productivity is improved, but reliability deteriorates due to malfeasant manipulation
Solution Approach 1:
The system performs preliminary validation of training data using Huber loss estimation before the data is used for predictive modeling. This advance detection mechanism identifies manipulated data points by comparing predicted values against historical patterns, preventing compromised data from entering the training pipeline and thereby maintaining both productivity and reliability.
Solution Approach 2:
The patent introduces a Huber loss estimator as an intermediary component between data retrieval and predictive modeling. This mediator validates training data by detecting anomalies through loss estimation, acting as a gatekeeper that filters out manipulated data while allowing legitimate data to pass through, thus resolving the contradiction between efficient processing and security.
2Reliability
If data validation mechanisms are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The system employs Huber loss estimation, which changes the evaluation parameter from standard mean squared error to a more robust loss function that is less sensitive to outliers and manipulated data. This parameter change enables effective validation without requiring complex validation architectures, as the Huber loss function inherently provides anomaly detection capabilities through its mathematical properties.
3Productivity
If manipulated training data is used, then productivity is maintained, but manufacturing precision deteriorates due to reduced model accuracy
Solution Approach 1:
The patent replaces traditional mechanical data validation methods with a mathematical approach using Huber loss estimation. Instead of complex filtering mechanisms or manual review processes, the system uses loss function-based anomaly detection to identify manipulated data, maintaining fast processing speeds while ensuring high precision in model generation through mathematically rigorous validation.
Data Source
AI summary
Embodiments of the present invention provide a system for generating data models secure from malfeasant manipulation for use in predictive modeling. The system is configured for retrieving training data associated with predictive modeling from a data source, processing the training data retrieved from the data source, transmitting the training data to a local linear model to generate a predictive output, retrieving historical data from the data source, transmitting the predictive output from the local linear model and the historical data retrieved from the data source to a Huber loss estimator module, validating, via the Huber loss estimator module, the predictive output received from the local linear model based on the historical data retrieved from the data source, and determining, via the Huber loss estimator module, if the training data has been manipulated by a malfeasant actor based on validating the one or more data points associated with the predictive output.