Prompt Embedding Clustering for Privacy-Preserving LLM Usage Insights
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Solution Overview
Problem
Developers of language models face challenges in deriving insights from user prompts due to privacy concerns, as these prompts often contain sensitive data, preventing effective training and development of new models or tools tailored to common use cases.
Innovation Solution
A privacy-preserving system that anonymizes user prompts using a prompt anonymization pipeline, storing them in a privacy-protecting datastore, generating embedding vectors, clustering related prompts, and summarizing themes for analysis in a separate accessible datastore, allowing developers to gain insights without exposing user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If developers examine user prompts to gain insights into model usage, then model development and training can be improved, but user privacy is compromised due to sensitive data exposure
Solution Approach 1:
The patent extracts only the essential semantic information from prompts through embedding vectors and clustering, separating useful insights from sensitive personal data. This allows developers to analyze usage patterns without accessing the actual sensitive content of user prompts.
Solution Approach 2:
The system creates anonymized copies of prompts in the form of embedding vectors that preserve semantic meaning for analysis while removing sensitive information. These vector representations serve as substitutes for actual prompts, enabling insight generation without privacy violation.
2Loss of information
If all user prompts are made accessible for analysis, then comprehensive insights can be obtained, but the complexity of privacy protection systems increases
Solution Approach 1:
The patent segments the prompt analysis process into distinct stages: embedding generation, clustering, and theme extraction. This segmentation allows comprehensive analysis while maintaining privacy at each stage, reducing overall system complexity compared to attempting to analyze all raw prompts directly.
Solution Approach 2:
The system introduces embedding vectors as an intermediary between raw prompts and analysis. These vectors serve as a middle layer that preserves semantic information for comprehensive insights while inherently protecting privacy, simplifying the privacy protection mechanism.
3Object-affected harmful factors
If sensitive data is removed from prompts before analysis, then privacy is preserved, but the quality and accuracy of insights into model usage deteriorates
Solution Approach 1:
The patent transforms prompts from their original text form into embedding vector representations, changing the parameter space from discrete text tokens to continuous semantic vectors. This transformation preserves the essential semantic information needed for accurate usage insights while removing sensitive data inherent in the original text.
Solution Approach 2:
The system replaces direct text-based analysis with vector-based semantic analysis. This substitution allows for more accurate measurement of usage patterns through mathematical operations on embedding vectors, maintaining or even improving insight quality while enhancing privacy protection.
Data Source
AI summary
A data processing system implements receiving a plurality of natural language prompts for a language model; storing the prompts in a privacy protecting datastore in which the prompts are inaccessible from outside of the privacy protecting datastore; analyzing the prompts stored to generate embedding vectors representing the plurality of natural language prompts; storing the embedding vectors in the privacy protecting datastore; analyzing the embedding vectors using a clustering algorithm to generate clusters of related embedding vectors in the privacy protecting datastore; generating, for each cluster, theme information summarizing the embedding vectors of the cluster; and storing the theme information associated with each cluster of the plurality of clusters in a second datastore outside of the privacy protecting datastore, the second datastore being accessible for querying the data stored therein.