Local Node Model Update System for Privacy-Preserving Fraud Detection
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Solution Overview
Problem
Current analytical models for event detection, such as fraud detection, are slow to update and vulnerable to evasion by third-party agents, necessitating a rapid and privacy-preserving system for distributing and implementing model updates across multiple local nodes.
Innovation Solution
A system comprising local nodes with monitoring, diagnosis, and evaluation modules that autonomously detect significant changes in system data, generate model updates, and distribute them to other nodes while ensuring data privacy, using a central module to prioritize and manage updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of updated detection models is used, then data privacy is maintained, but the model update process becomes slow and time-consuming
Solution Approach 1:
The system enables automatic model updates through local nodes that autonomously evaluate incoming updates against their current models and data characteristics, eliminating the need for manual model creation and deployment while maintaining data privacy through local evaluation processes
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism where local nodes act as mediators between central model updates and local data, automatically assessing compatibility and applying updates without manual intervention while preserving data privacy through local-only evaluation
2Productivity
If rapid model updates are distributed across multiple local nodes, then productivity improves, but system complexity increases
Solution Approach 1:
The system divides the model update process into independent segments at each local node, where each node autonomously evaluates and applies updates locally without coordinating with other nodes, reducing system complexity while enabling parallel processing across multiple nodes
Solution Approach 2:
Each local node is equipped with specialized evaluation capabilities tailored to its local data characteristics, allowing independent decision-making about model updates without requiring centralized coordination, thus reducing overall system complexity while maintaining rapid update deployment
3Productivity
If automatic model updates are implemented, then productivity improves, but control over model application decreases
Solution Approach 1:
The system implements feedback mechanisms where local nodes automatically evaluate incoming model updates against their local data and performance metrics, providing continuous feedback on update effectiveness while maintaining autonomous control over when and how updates are applied
Data Source
AI summary
The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.