Anonymizer for Model Evaluation Using Candidate Jobs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating models struggle to maintain data privacy, especially when using historical data that may contain sensitive information, and often fail to adequately anonymize or aggregate data, leading to potential exposure of private information during model improvement and testing processes.
Innovation Solution
The system employs a method where a development environment deploys modules to customer environments to generate evaluation metrics, with an anonymizer ensuring that the data is sufficiently anonymized and aggregated to prevent exposure of private information, allowing for real-world data testing without breaching customer privacy through the use of anonymized and aggregated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical data is used as training data to improve models, then model performance is improved, but data privacy is compromised due to potential exposure of personal and privileged information
Solution Approach 1:
The patent introduces an intermediary anonymization layer between the historical data and the model training process. This intermediary component removes or obscures personally identifiable information and privileged details while preserving the essential patterns and relationships needed for model training, thus resolving the contradiction between utilizing historical data for improvement and protecting data privacy
Solution Approach 2:
The patent creates synthetic copies of historical data that capture the statistical properties and patterns necessary for training models while lacking the specific identifying characteristics of the original data. These synthetic datasets allow model improvement without exposing actual personal or privileged information from the source data
2Object-affected harmful factors
If conventional aggregation and anonymization techniques are applied to protect data privacy, then data privacy is improved, but the ability to generate comparable data for evaluating model performance is compromised
Solution Approach 1:
The patent transforms the parameters of data aggregation and anonymization to achieve an optimal balance. By adjusting aggregation thresholds, anonymization sensitivity levels, and data transformation parameters, the system preserves sufficient data quality and comparability for accurate model evaluation while maintaining adequate privacy protection
Solution Approach 2:
The patent implements dynamic aggregation and anonymization processes that adapt to different evaluation scenarios and data characteristics. The system dynamically adjusts the degree of aggregation and anonymization based on the specific model being evaluated and the sensitivity of the data, ensuring both privacy protection and evaluation accuracy are maintained
3Measurement precision
If direct access to customer environment is used for testing new functionality, then testing accuracy is improved, but data privacy is compromised due to potential exposure of private information
Solution Approach 1:
The patent introduces an intermediary testing framework that acts as a mediator between the testing system and the customer environment. This intermediary layer enables accurate testing of new functionality by executing test cases through controlled interfaces while preventing direct access to and exposure of private customer data, thus resolving the contradiction between testing accuracy and data privacy protection
Data Source
AI summary
Systems and methods for evaluating performance of experimental code using a candidate jobs framework are provided. The techniques may include obtaining an indication of a module under evaluation; configuring a customer environment to provide an evaluation compute, separate from a customer compute executing customer-directed jobs, for executing the module under evaluation; deploying the module under evaluation in the customer environment, wherein deploying the module under evaluation causes the customer environment to execute the module under test using the evaluation compute; configuring the evaluation compute to operate under predetermined test conditions based on a script associated with the module under evaluation; and obtaining an evaluation metric from the customer environment, wherein the evaluation metric is representative of an execution of the module under evaluation based on the predetermined test conditions.