Signal Aggregation Labeling for Ground Truth User Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identity management systems face challenges in accurately evaluating the efficacy of risk assessment products due to limited access to ground truth knowledge, leading to inefficiencies in user risk classification.
Innovation Solution
The system collects data signals from multiple sources, stores them in a database, and assigns labels to users as malicious or benign, using these labels to evaluate the efficacy of risk assessment products by comparing outputs with ground truth data, calculating confidence levels, and aggregating data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the identity management system uses heuristics or models to assess user risk, then risk classification can be performed, but the accuracy and efficacy of the assessment are limited due to lack of ground truth knowledge
Solution Approach 1:
The system performs preliminary actions by collecting and storing data signals from multiple sources before the actual risk assessment is needed. This pre-collected data serves as ground truth that can be used to evaluate and validate the risk assessment models, thereby improving measurement precision without requiring ground truth knowledge at the moment of assessment
Solution Approach 2:
The patent introduces an intermediary mechanism - a database storing aggregated data signals from multiple sources - that mediates between the risk assessment models and the need for ground truth validation. This intermediary allows the system to evaluate model accuracy by comparing predictions against pre-collected signal aggregations, effectively bridging the gap caused by lacking direct ground truth knowledge
2Measurement precision
If the system collects and aggregates data from multiple sources to establish ground truth, then assessment accuracy improves, but system complexity increases
Solution Approach 1:
The database component is designed with multi-functionality, serving both as a storage repository for raw data signals and as an evaluation framework for validating risk assessment models. This universal component handles multiple functions - data collection, aggregation, storage, and validation - thereby improving ground truth establishment accuracy without proportionally increasing system complexity
Solution Approach 2:
The system employs self-service mechanisms where the aggregated data signals automatically serve as ground truth for evaluating the risk assessment models. The same data collection infrastructure that gathers signals also provides the validation framework, allowing the system to self-evaluate its accuracy without requiring external validation systems, thus improving precision while limiting complexity growth
Data Source
AI summary
An identity management system may perform ground truth establishment and labeling techniques using signal aggregation. The identity management system may obtain, from multiple data sources, multiple data signals associated with a user of a set of multiple users of the identity management system. The identity management system may store the multiple data signals in a database. In some examples, the identity management system may aggregate the multiple data signals in the database. The identity management system may assign a label to the user based on the database. The label may indicate whether the user is malicious or benign. The identity management system may calculate a confidence level for a risk assessment product based on a comparison between the label and one or more outputs of the risk assessment product. The confidence level may indicate a confidence of the risk assessment product to classify the user as malicious or benign.