# SharedRequest: A Privacy-Preserving Inference Framework for Large Language Models

> This article introduces the SharedRequest project, a model-agnostic privacy-preserving inference solution that helps users hide sensitive information when using large language models while maintaining query effectiveness.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-06T00:06:11.000Z
- 最近活动: 2026-05-06T01:59:17.637Z
- 热度: 147.1
- 关键词: 隐私保护, 大语言模型, 数据脱敏, 模型无关, PII保护, 推理安全, 判别模型
- 页面链接: https://www.zingnex.cn/en/forum/thread/sharedrequest-daa73fb6
- Canonical: https://www.zingnex.cn/forum/thread/sharedrequest-daa73fb6
- Markdown 来源: floors_fallback

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## SharedRequest: Introduction to a Model-Agnostic Privacy-Preserving Inference Framework for LLMs

This article introduces the SharedRequest project, a model-agnostic privacy-preserving inference solution designed to address the problem of sensitive information leakage (such as medical records, financial data, and trade secrets) in the use of large language models (LLMs). This solution processes sensitive information locally before sending queries, allowing users to regain control of their data, and is compatible with any LLM service (including OpenAI API, open-source models, and enterprise self-hosted solutions).

## Current State of LLM Privacy Risks and Challenges of Traditional Methods

As LLMs are integrated into daily work (e.g., doctors summarizing medical records, lawyers analyzing contracts), sensitive information flows to third parties or administrators via APIs or internal deployments, posing leakage risks. Traditional privacy protection methods like differential privacy (requiring intervention in the training phase and unable to protect inference of deployed models) and homomorphic encryption (high computational overhead and difficult for real-time interaction) face challenges.

## Core Mechanism of SharedRequest: Model-Agnostic Discriminative Privacy-Preserving Process

SharedRequest operates as a pre-filter without modifying the underlying LLM, with a core process divided into three stages:
1. The discriminative model identifies sensitive entities (including PII and implicit sensitive information such as disease types);
2. Transform sensitive content (generalization, hashing, synthetic replacement—strategies are selected based on scenarios);
3. Send the desensitized query to the LLM, and reverse-map the response to restore it. Sensitive information remains local to the user at all times.

## Optimization of Discriminative Model Training and Deployment Options

The discriminative model is trained on labeled sensitive text, with a lightweight architecture supporting consumer-grade hardware (16GB RAM + 8GB NVIDIA GPU). It supports incremental learning (users can fine-tune to adapt to industry compliance standards like HIPAA and GDPR) and active learning (prompting users to label when uncertain). Deployment is flexible: personal desktop applications, enterprise gateway services, developer API integration, and compatible with future LLM models.

## Trade-off Strategies Between Privacy and Utility

The system provides preset strategy templates: strict mode (maximum desensitization), balanced mode (balancing protection and context), and minimal mode (only processing obvious PII). Users can dynamically adjust strategies—for example, doctors keep medical terms while desensitizing patient identities when querying, and lawyers keep contract clauses while desensitizing party information when analyzing contracts—enabling fine-grained control.

## Limitations, Future Directions, and Domain Contributions

Limitations: The accuracy of the discriminative model affects protection effectiveness (missed detection/over-detection), and some queries are difficult to desensitize. Future directions: Multilingual support, intelligent synthetic data, and integration with federated learning. Domain contributions: A pragmatic solution推动 privacy technology from research to production, emphasizing that privacy protection requires collaboration between technology, processes, and people.

## Conclusion: Privacy Protection Philosophy and Recommendations

SharedRequest represents the concept of user data control—technological progress should not come at the cost of privacy. It is recommended that decision-makers for LLM deployment evaluate this tool, as it is a solid step towards balancing privacy and convenience.