Synthetic Prompt Generation for Privacy-Safe AI Model Debugging
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Solution Overview
Problem
Existing AI model development processes compromise user privacy by exposing sensitive information in user prompts, hindering effective debugging and improvement due to the need for direct access to prompts and outputs.
Innovation Solution
A system utilizing a confidential computing environment and a trained synthetic prompt generator to create anonymized prompts, allowing developers to analyze AI model behavior without exposing sensitive user information, using techniques like differential privacy and fine-tuning to generate synthetic prompts that mimic user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If developers access original user prompts and LLM outputs for debugging and improvement, then the AI model can be effectively refined and enhanced, but user privacy is compromised and sensitive information is exposed
Solution Approach 1:
The patent creates synthetic copies of user prompts that preserve the structural and linguistic characteristics of real prompts while removing all sensitive information. These synthetic prompts serve as safe substitutes for debugging and analysis, allowing developers to study model behavior without accessing actual user data. The copying principle enables the separation of analytical value from sensitive content.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms original user prompts into synthetic prompts before they reach developers. This intermediary system uses trained AI models to generate synthetic versions that maintain the essential features needed for debugging while acting as a barrier that prevents direct exposure of sensitive user information. The intermediary preserves the utility of the data for development purposes while eliminating privacy risks.
2Object-affected harmful factors
If sensitive information is removed from prompts through redaction, then user privacy is protected, but the quality and usefulness of debugging data is degraded
Solution Approach 1:
Instead of simply redacting or removing sensitive information from original prompts, the system creates complete synthetic copies that preserve the full contextual structure, language patterns, and semantic meaning of the original prompts. These synthetic copies maintain all the information needed for effective debugging while containing no actual sensitive user data, thereby avoiding the information loss that occurs with traditional redaction methods.
Solution Approach 2:
The system transforms the sensitive information parameter while preserving other critical parameters such as prompt structure, language style, and contextual relationships. By changing only the sensitive content parameters and maintaining all other parameters intact, the synthetic prompts retain their full debugging utility without containing actual sensitive information. This selective parameter transformation avoids the degradation that occurs with blanket rediction approaches.
Data Source
AI summary
A system for development of an Artificial Intelligence (AI) model while protecting sensitive user information includes: a confidential computing environment in which original prompts to the AI model written by users are collected; a trained synthetic prompt generator to generate synthetic prompts based on the original prompts, wherein the synthetic prompt generator generates anonymized synthetic prompts without sensitive user information identifiable from the original prompts; and a developer computing environment in which the synthetic prompts are submitted to the AI model under development to generate a dataset that includes the synthetic prompts and corresponding AI model output for analysis to determine updates for the AI model while protecting the sensitive user information of actual users.