LLM Gateway Routing for Anonymous Prompt Privacy Protection
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Solution Overview
Problem
Large language models (LLMs) pose significant risks to user privacy due to the disclosure of personal information during interactions, as users are often required to provide credentials that can trace their prompts back to them, and LLMs retain and potentially expose confidential information.
Innovation Solution
Implementing an LLM gateway that routes user prompts through client servers with their own access credentials, geographically distributed and ranked based on privacy levels, to anonymize user location and analyze prompts for sensitivity, redacting unnecessary private information before sending them to appropriate LLM providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users provide access credentials directly to LLM servers, then authentication and access control are simplified, but user privacy is compromised as prompts can be traced back to users
Solution Approach 1:
The patent introduces client servers as intermediaries between users and LLM servers. These client servers hold access credentials and submit prompts on behalf of users, preventing direct tracing of prompts to user identities while maintaining authentication functionality. The client server acts as a mediator that decouples the authentication process from the prompt submission process.
2Productivity
If LLMs retain user prompts for model training, then model improvement and learning are enhanced, but confidential information may become publicly accessible
Solution Approach 1:
The patent implements differential privacy protection where different levels of privacy are applied to different prompts based on their sensitivity. Prompts are analyzed to determine sensitivity levels, and appropriate privacy measures are applied locally to each prompt. This allows model training to proceed on less sensitive prompts while protecting confidential information in sensitive prompts.
Solution Approach 2:
The patent modifies the training data parameters by adding noise or transforming sensitive information before inclusion in training datasets. This changes the parameters of the training data to maintain utility for model improvement while removing personally identifiable or confidential information that could lead to privacy breaches.
3Object-affected harmful factors
If client servers are geographically distributed, then user location privacy is protected, but system complexity and coordination overhead increase
Solution Approach 1:
The patent designs client servers with universal functionality that can operate in any geographic location. Each client server implements the same core functions of holding credentials, analyzing prompt sensitivity, and submitting to appropriate LLM servers. This multi-functional design allows geographic distribution without requiring different server types or complex location-specific configurations.
4Object-affected harmful factors
If prompts are analyzed for sensitivity before submission, then private information protection is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements partial sensitivity analysis by focusing on detecting specific types of sensitive information (personal identifiers, confidential data patterns) rather than analyzing every aspect of each prompt. This selective approach provides adequate privacy protection for the most critical information types while reducing overall processing time and computational overhead compared to comprehensive analysis of all prompt content.
Data Source
AI summary
Systems and methods for protecting private information during large language module (LLM) interactions. In one embodiment, an LLM gateway may route user prompts through a client server that possesses its own access credentials to an LLM server. The access credentials of the client server may be separate from and unassociated with the user. The client server may access the LLM using its own access credentials, submit the user's prompt to the LLM server, and return the results to the user. Routing user prompts through a client server that possesses its own LLM access credentials may help to ensure that user prompts cannot be traced back to the user or to personal information that a user disclosed in order to gain access to an LLM server.