Executable Script for Secure Training Data Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning model developers face challenges in evaluating the suitability of training data without accessing the data or revealing model details, as existing methods require large datasets and direct interaction with data providers, limiting the ability to assess data quality prior to purchase.
Innovation Solution
A system generates an executable file containing a model script and metrics script, which is executed by the data provider to generate model performance metrics, allowing evaluation of training data quality without exposing the model or accessing the data, using a cloud-based architecture that compiles scripts into a binary format to obscure details and facilitate secure evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a developer acquires training data from a data provider to evaluate model suitability, then the developer can train and evaluate a model to assess data quality, but the developer must access the training data and the data provider must provide it, which is unlikely to happen prior to purchase
Solution Approach 1:
The patent introduces an intermediary evaluation system where the developer sends a model script (not the actual model) to the data provider, who executes it locally to generate performance metrics. This intermediary process allows evaluation without direct data sharing or model disclosure, resolving the contradiction between needing accurate evaluation and protecting sensitive information.
Solution Approach 2:
Instead of sharing the actual machine learning model, the developer creates and sends a copy in the form of a model script or binary file that encapsulates the evaluation logic. This copy enables the data provider to execute evaluations locally while the original model remains protected, allowing assessment without exposure of proprietary assets.
2Reliability
If a developer receives only a data sample from a data provider prior to purchase, then the data provider protects its data, but the sample is too small to be used for model training and the feature distribution is unknown
Solution Approach 1:
The system uses an intermediary evaluation mechanism where the data provider executes a model script locally against their full dataset and returns only aggregated performance metrics. This intermediary process enables comprehensive evaluation using the complete dataset while maintaining data security, as the raw data never leaves the provider's environment.
Solution Approach 2:
The patent replaces the traditional mechanical approach of physically sharing data samples with a digital intermediary system. Instead of transferring data or models directly, the system uses executable scripts that perform computations locally and return only results, substituting physical data exchange with a secure computational intermediary process.
3Ease of operation
If a developer needs to evaluate model performance metrics to determine purchasing decision, then the developer can make informed decisions, but the evaluation process requires access to both the training data and the model, which creates security and privacy concerns
Solution Approach 1:
The patent introduces an intermediary evaluation architecture where a model script acts as a mediator between the developer's evaluation needs and the data provider's data security requirements. The script executes locally at the provider's system, performing evaluations without requiring direct access to sensitive data or models, thus simplifying the purchasing decision process while maintaining security.
Solution Approach 2:
The developer creates a copy of the model in the form of an executable script or binary file that contains the evaluation logic. This copy can be safely transmitted to the data provider for local execution, enabling comprehensive performance evaluation without exposing the original proprietary model, thus facilitating easier purchasing decisions with reduced security concerns.
Data Source
AI summary
Systems and methods provide determination of a model script for training a first machine learning model based on input training data, determination of a metrics script for determining one or more performance metric values associated with the trained first machine learning model based on validation data, and compilation of the model script and the metrics script into an executable file.